U.S. patent application number 14/264671 was filed with the patent office on 2014-11-27 for utility metering.
This patent application is currently assigned to ISIS INNOVATION LIMITED. The applicant listed for this patent is ISIS INNOVATION LIMITED. Invention is credited to James Donaldson, Malcolm McCulloch.
Application Number | 20140347077 14/264671 |
Document ID | / |
Family ID | 41314706 |
Filed Date | 2014-11-27 |
United States Patent
Application |
20140347077 |
Kind Code |
A1 |
Donaldson; James ; et
al. |
November 27, 2014 |
UTILITY METERING
Abstract
An apparatus has an input section arranged to receive values
representative of the total instantaneous supply of electrical
current as a function of time from an alternating voltage supply.
Current waveforms comprising sets of values representative of the
cyclic waveform of the electric current supply are obtained. A
delta waveform generator calculates the difference between a
current waveform and an earlier current waveform. An edge detector
is arranged to detect an edge or edges in the delta waveform. An
analysis section is arranged to identify at least one appliance
load based at least on information on the edge or edges detected by
the edge detector, and to determine the electrical energy consumed
by said appliance load. Another apparatus has an input section
arranged to receive values representative of the current supplied
to an installation, such as a house. A store contains appliance
data characteristic of the use of electricity by each of a
plurality of appliances. A processor is arranged to analyse the
received values to detect when an appliance is switched on and
determine the fractional change in resistance of a heating
appliance from the when it is switched on until it reaches its
operating temperature. This information is used to identify what
the particular appliance is, and to determine the electrical energy
consumption by that appliance. A utility meter for metering the use
of at least one utility supplied to a plurality of appliances is
also disclosed. An input section is arranged to receive values
representative of the use of a first utility. A store contains
appliance data characteristic of the use of utilities by each of a
plurality of appliances. A processor is arranged to analyse the
received values and to determine information on the use of a second
utility by each appliance, based on the received values and
appliance data.
Inventors: |
Donaldson; James; (Oxford,
GB) ; McCulloch; Malcolm; (Oxford, GB) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ISIS INNOVATION LIMITED |
Oxford |
|
GB |
|
|
Assignee: |
ISIS INNOVATION LIMITED
Oxford
GB
|
Family ID: |
41314706 |
Appl. No.: |
14/264671 |
Filed: |
April 29, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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13003709 |
Jan 11, 2011 |
8843334 |
|
|
PCT/GB2009/001754 |
Jul 17, 2009 |
|
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14264671 |
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Current U.S.
Class: |
324/691 |
Current CPC
Class: |
G01R 21/133 20130101;
Y04S 20/38 20130101; Y04S 20/30 20130101; G01R 27/02 20130101; G01D
4/00 20130101; G01R 21/1331 20130101 |
Class at
Publication: |
324/691 |
International
Class: |
G01R 21/133 20060101
G01R021/133; G01R 27/02 20060101 G01R027/02 |
Foreign Application Data
Date |
Code |
Application Number |
Jul 17, 2008 |
GB |
0813143.5 |
Oct 28, 2008 |
GB |
0819763.4 |
Nov 13, 2008 |
GB |
0820812.6 |
Claims
1. A variable power load detector apparatus, for use in a
non-intrusive electrical load meter for metering the use of
electricity supplied to a plurality of loads, the apparatus
comprising: an input section arranged to receive values
representative of the total instantaneous supply of electrical
current as a function of time from an alternating voltage supply; a
monitor section arranged to determine current waveforms comprising
sets of values representative of the cyclic waveform of the
electric current supply; a delta waveform generator arranged to
calculate the difference between a current waveform and an earlier
current waveform, by subtracting the respective sets of values
determined by the monitor section, to obtain a delta waveform; an
edge detector arranged to detect an edge or edges in the delta
waveform; and an analysis section arranged to identify at least one
load based at least on information on the edge or edges detected by
the edge detector.
2. Apparatus according to claim 1, further comprising an event
detector arranged to detect an event representing a change in the
total electrical energy being supplied per cycle; and wherein the
delta waveform generator is arranged to calculate the difference
between the current waveforms before and after the detected
event.
3. Apparatus according to claim 1 or 2, wherein the edge detector
is further arranged to determine information on the gradient of at
least one of the edges.
4. Apparatus according to claim 1, 2 or 3, wherein the edge
detector is further arranged to determine information on the
position of any edges in the delta waveform.
5. Apparatus according to claim 4, wherein the analysis section is
further arranged to determine the nominal full power of the at
least one load based on the position of at least one detected
edge.
6. Apparatus according to any one of the preceding claims, wherein
the edge detector is further arranged to determine information on
the number of edges in the delta waveform.
7. Apparatus according to any one of the preceding claims, wherein
the analysis section is further arranged to identify at least one
load based on the presently known powers of loads known to the
apparatus.
8. Apparatus according to any one of the preceding claims, wherein
the monitor section is arranged to determine current waveforms for
the whole or half of a cycle of the alternating electricity
supply.
9. Apparatus according to any one of the preceding claims, wherein
at least one current waveform determined by the monitor section is
a weighted mean over a plurality of cycles of the alternating
electricity supply.
10. Apparatus according to any one of the preceding claims, wherein
at least one load has its power varied by a controlled switch;
preferably said controlled switch comprises a TRIAC, an SCR or a
thyristor.
11. Apparatus according to any one of the preceding claims, wherein
the analysis section is arranged to determine the electrical energy
consumed individually by each load.
12. Apparatus according to claim any one of the preceding claims,
wherein the edge detector is arranged to: correlate the delta
waveform with at least a subset of basis waveforms, each basis
waveform having a known edge or edges; identify the basis waveform
corresponding to the highest correlation coefficient; and detect an
edge or edges in the delta waveform based on the known edge or
edges in the identified basis waveform.
13. Method for detecting a variable power load, for use in
non-intrusive electrical load metering, for metering the use of
electricity supplied to a plurality of loads, the method
comprising: receiving values representative of the total
instantaneous supply of electrical current as a function of time
from an alternating voltage supply; determining current waveforms
comprising sets of values representative of the cyclic waveform of
the electric current supply; generating a delta waveform by
calculating the difference between a current waveform and an
earlier current waveform, by subtracting the respective sets of
waveform values; detecting an edge or edges in the delta waveform;
and identifying at least one load based at least on information on
the detected edge or edges.
14. Method according to claim 13, further comprising detecting an
event representing a change in the total electrical energy being
supplied per cycle; and wherein the delta waveform is calculated as
the difference between the current waveforms before and after the
detected event.
15. Method according to claim 13 or 14, further comprising
determining information on the gradient of at least one of the
edges.
16. Method according to claim 13, 14 or 15, further comprising
determining information on the position of any edges in the delta
waveform.
17. Method according to claim 16, further comprising determining
the nominal full power of the at least one load based on the
position of at least one detected edge.
18. Method according to any one of claims 13 to 17, further
comprising determining information on the number of edges in the
delta waveform.
19. Method according to any one of claims 13 to 18, further
comprising identifying at least one load based on the presently
known powers of loads being supplied with electricity.
20. Method according to any one of claims 13 to 19, wherein the
current waveforms are determined for the whole or half of a cycle
of the alternating electricity supply.
21. Method according to any one of claims 13 to 20, wherein at
least one current waveform is an average over a plurality of cycles
of the alternating electricity supply.
22. Method according to any one of claims 13 to 21, wherein at
least one load has its power varied by a controlled switch;
preferably said controlled switch comprises a TRIAC, an SCR or a
thyristor.
23. Method according to any one of claims 13 to 22, further
comprising determining the electrical energy individually consumed
by each load.
24. Method according to any one of claims 13 to 23, wherein the
step of detecting an edge or edges in the delta waveform comprises:
providing a set of basis waveforms, each basis waveform having a
known edge or edges; correlating the delta waveform with at least a
subset of the basis waveforms; identifying the basis waveform
corresponding to the highest correlation coefficient; and detecting
an edge or edges in the delta waveform based on the known edge or
edges in the identified basis waveform.
25. Apparatus for metering the use of electricity supplied to a
plurality of appliances, the apparatus comprising: an input section
arranged to receive values representative of the total supply of
electrical power as a function of time; a transient detector
arranged to detect the time at which an appliance is switched on
from a change in the received values due to an increase in the
electric power being supplied at that time; an analysis section
arranged to analyse the received values and to determine: (i) a
first value related to the resistance of said appliance at the time
of being switched on detected by said transient detector; and (ii)
a second value related to the resistance of said appliance when
operating in a steady state; and a processing section arranged to
identify said appliance based on at least said first and second
values, and to determine the electrical energy consumed by said
appliance.
26. Apparatus according to claim 25, wherein the received values
are measurements of current or measurements of current and voltage
of the electricity supply.
27. Apparatus according to claim 25 or 26, wherein each said value
related to the resistance is one of: the current; the reciprocal of
the current; the voltage divided by the current; the voltage
multiplied by the current, and wherein said current values
represent the current supplied to said appliance given by the total
supply current minus the supply current before the switch on of
said appliance detected by the transient detector.
28. Apparatus according to claim 25, 26 or 27, wherein the
processing section is arranged to calculate a classification value
given by: the difference between the second value and the first
value, divided by the first value.
29. Apparatus according to any one of claims 25 to 28 wherein the
analysis section is further arranged to determine the time duration
from when the appliance is switched on until the electrical power
being used by the appliance has reached a steady state; and the
identification of said appliance by said processing section is
further based on said time duration.
30. Apparatus according to claim 29, wherein the analysis section
is arranged to determine a further classification value related to:
the total electrical energy supplied to the appliance from the time
of switch on until the time steady state is reached, minus the
product of the steady state power and the time from switch on until
steady state is reached; and the identification of said appliance
by said processing section is further based on said further
classification value.
31. Apparatus for metering the use of electricity supplied to a
plurality of appliances, the apparatus comprising: an input section
arranged to receive values representative of the total supply of
electrical power as a function of time; a transient detector
arranged to detect the time at which an appliance is switched on
from a change in the received values due to an increase in the
electric power being supplied at that time; an analysis section
arranged to analyse the received values and to determine: (i) the
time when the electrical power being used by the appliance has
reached a steady state; and (ii) a classification value related to:
the total electrical energy supplied to the appliance from the time
of switch on until the time steady state is reached, minus the
product of the steady state power and the time from switch on until
steady state is reached; a processing section arranged to identify
said appliance based on at least said classification value, and to
determine the total electrical energy consumed by said
appliance.
32. Apparatus according to any one of claims 25 to 31, wherein
electricity is supplied to a plurality of appliances and said
processor is arranged to determine information on the electricity
usage by individual ones of said appliances.
33. Method for metering the use of electricity supplied to a
plurality of appliances, the method comprising: receiving values
representative of the total supply of electrical power as a
function of time; detecting the time at which an appliance is
switched on from a change in the received values due to an increase
in the electric power being supplied at that time; analysing the
received values and determining: (i) a first value related to the
resistance of said appliance at the time of being switched on; and
(ii) a second value related to the resistance of said appliance
when operating in a steady state; and identifying said appliance
based on at least said first and second values, and determining the
electric energy consumed by said appliance.
34. Method according to claim 33, wherein the received values are
measurements of current or measurements of current and voltage of
the electricity supply.
35. Method according to claim 33 or 34, wherein each said value
related to the resistance is one of: the current; the reciprocal of
the current; the voltage divided by the current; the voltage
multiplied by the current, and wherein said current values
represent the current supplied to said appliance given by the total
supply current minus the supply current before the switch on of
said appliance.
36. Method according to claim 33, 34 or 35, further comprising
calculating a classification value given by: the difference between
the second value and the first value, divided by the first
value.
37. Method according to any one of claims 33 to 36, further
comprising determining the time duration from when the appliance is
switched on until the electrical power being used by the appliance
has reached a steady state; and wherein the identification of said
appliance is further based on said time duration.
38. Method according to claim 37, comprising determining a further
classification value related to: the total electrical energy
supplied to the appliance from the time of switch on until the time
steady state is reached, minus the product of the steady state
power and the time from switch on until steady state is reached;
and wherein the identification of said appliance is further based
on said further classification value.
39. Method for metering the use of electricity supplied to a
plurality of appliances, the method comprising: receiving values
representative of the total supply of electrical power as a
function of time; detecting the time at which an appliance is
switched on from a change in the received values due to an increase
in the electric power being supplied at that time; analysing the
received values and determining: (i) the time when the electrical
power being used by the appliance has reached a steady state; and
(ii) a classification value related to: the total electrical energy
supplied to the appliance from the time of switch on until the time
steady state is reached, minus the product of the steady state
power and the time from switch on until steady state is reached;
and identifying said appliance based on at least said
classification value, and determining the total electrical energy
consumed by said appliance.
40. Method according to any one of claims 33 to 39, wherein
electricity is supplied to a plurality of appliances and said
method is arranged to determine information on the electricity
usage by individual ones of said appliances.
41. Apparatus for metering the use of a utility, the apparatus
comprising: an input section arranged to receive values
representative of use of a first utility; and a processor arranged
to analyse the received values and to determine information on the
use of a second utility based on the received values; and an output
section for outputting said information.
42. Apparatus according to claim 41, wherein said input section is
further arranged to receive values representative of total use of
the second utility.
43. Apparatus according to claim 42, wherein said second utility is
supplied to a plurality of appliances and said processor is further
arranged to determine information on the usage of said second
utility by individual ones of said appliances.
44. Apparatus according to claim 43, wherein the processor is
arranged to determine information on the use of the second utility
by each specific appliance based on inference of the most probable
appliance or combination of appliances to be operating based on the
received values.
45. Apparatus according to any one of claims 41 to 44, wherein the
processor is arranged to determine information on the use of the
second utility further using known characteristics of the or each
appliance to which said second utility is supplied.
46. Apparatus according to any one of claims 41 to 45, wherein said
received values represent the supply of a utility as a function of
time.
47. Apparatus according to claim 46, wherein one of said utilities
is electricity and the input values represent at least
instantaneous current of the supply, optionally both instantaneous
current and voltage of the supply.
48. Apparatus according to any one of claims 41 to 47, wherein the
first and second utilities respectively comprise one of the
following pairs: water and electricity; electricity and oil; or gas
and electricity.
49. Method for metering the use of a utility, comprising: receiving
values representative of use of a first utility; and analysing the
received values to determine information on the use of a second
utility based on the received values; and outputting said
information.
50. Method according to claim 49, wherein further comprising
receiving values representative of total use of the second
utility.
51. Method according to claim 50, wherein said second utility is
supplied to a plurality of appliances, and wherein said method
further comprises determining information on the usage of said
second utility by individual ones of said appliances.
52. Method according to claim 51, wherein the analysing process
comprises determining information on the use of the second utility
by each specific appliance based on inference of the most probable
appliance or combination of appliances to be operating based on the
received values.
53. Method according to any one of claims 49 to 52, wherein
information on the use of the second utility is determined further
using known characteristics of the or each appliance to which said
second utility is supplied.
54. Method according to any one of claims 49 to 53, wherein said
received values represent the supply of a utility as a function of
time.
55. Method according to claim 54, wherein one of said utilities is
electricity and the input values represent at least instantaneous
current of the supply, optionally both instantaneous current and
voltage of the supply.
56. Method according to any one of claims 49 to 55, wherein the
first and second utilities respectively comprise one of the
following pairs: water and electricity; electricity and oil; or gas
and electricity.
57. A computer program comprising computer-executable code that
when executed on a computer system, causes the computer system to
perform a method according to any one of claims 13 to 24, or 3 to
40, or 49 to 56.
58. A computer-readable medium storing a computer program according
to claim 57.
59. A computer program product comprising a signal comprising a
computer program according to claim 57.
60. An apparatus for metering the use of a utility supplied to a
plurality of appliances, the apparatus comprising: an input section
arranged to receive, from a sensor, values representative of the
total utility being used as a function of time, and to receive time
data on the actual time of each sensed value; a store containing
appliance data characteristic of the use of the utility by each of
a plurality of appliances; a processor arranged to analyse the
received values and time data, based on the appliance data, and to
determine information on the use of the utility by each appliance;
and an output section for outputting said information; wherein the
processor is arranged to determine information on the use of the
utility by each appliance based on inference of the most probable
appliance or combination of appliances to be operating at a
particular time and the most probable magnitude of consumption of
the utility by each respective appliance, based on the received
values, time data and appliance data; and wherein the inference
uses an inference technique selected from Bayesian inference,
neural networks and fuzzy logic.
61. The apparatus of claim 60, in which the processor is arranged
to use the inference to calculate a probability of the plurality of
appliances being in a particular state.
62. The apparatus of claim 61, in which the processor is arranged
to: use the inference to calculate a plurality of probabilities,
each probability being a probability that the plurality of
appliances are in a respective state; and determine that the
plurality of appliances are in the state with the highest
probability.
Description
FIELD OF THE INVENTION
[0001] The present invention concerns an apparatus for metering the
use of a utility, such as electricity, gas, oil or water, supplied
to one or more appliances. For example, when the utility is
electricity, the present invention concerns determining the
electrical power consumed by one or more individual appliances
among the plurality of appliances, e.g. by detecting a variable
power load.
BACKGROUND OF THE INVENTION
[0002] There is an increasing concern to reduce the consumption of
resources, both at a domestic level in residential buildings, and
at a commercial level in offices, shops, factories and so forth.
The reasons for this are both to save costs and also because of
concerns for the environment, such as the conservation of scarce
resources, for example water in regions where rainfall is low, to
reduce CO.sub.2 emissions, and to conserve finite resources such as
coal, gas and oil.
[0003] Conventionally, consumers receive bills from utility
companies that may indicate the quantity of the utility used since
the last bill, for example monthly or quarterly, based on periodic
meter readings or even based on estimates of consumption since the
last meter reading. For example, in the case of electricity supply,
the information is presented to the consumer in terms of the number
of kilowatt hours of electrical energy that has been used, which is
meaningless to many people, and gives very little idea about how
they are actually using the energy and where they can cut back.
Studies have shown that the effect of providing consumers with
real-time detailed information about the energy they are using is
that their consumption reduces by up to 20%. In order to provide
this information, it is necessary to identify where the energy
drawn from this supply is ending up, i.e. which appliances are
being used, how much and when. It is a problem to provide this
information.
[0004] Devices are known which can be plugged into a conventional
electricity outlet socket that can monitor the energy consumption
by a particular appliance (an appliance will also be referred to
herein more generally as an electrical load or simply a `load`)
plugged into that socket. However, this information is inconvenient
to obtain, and for fully monitoring the consumption at a particular
site, such as a house, a separate metering device would have to be
plugged into every socket to monitor every appliance, and it is
generally not possible to connect such metering devices to
permanently-wired appliances, such as cookers, which are typically
some of the largest consumers of energy. Lighting also accounts for
a significant amount of energy usage in domestic residences, for
example on average 20% of the typical electricity bill in the UK is
spent on lighting. Much lighting is provided in permanently wired
light fittings, so a non-intrusive monitoring system is desired in
this case.
[0005] Non-Intrusive Appliance Load Monitoring (NIALM) systems are
known which attempt to detect signatures in the supply of the
utility that are characteristic of particular appliances,
including, for example, monitoring to detect events when appliances
are switched on or off. For example, U.S. Pat. No. 4,858,141 (Hart
et al.) discloses monitoring the voltage and current of the
electricity supply to a residence to try to determine which
appliances are running at any particular time and to determine the
energy consumed by each.
[0006] However, distinguishing between certain types of load can be
difficult in some cases.
[0007] For example, dimming devices (also called dimmer switches)
are often fitted to lighting systems to allow variable control of
the lighting level. These dimmer switches present a significant
challenge to electricity usage monitoring systems because they
transform a load that is nominally resistive and of fixed power, to
a continuously variable power load, which additionally has a
variable reactive power dependent on the level of dimming. There is
a problem in providing a reliable way of distinguishing such loads
and of measuring the power consumed by this class of device.
[0008] U.S. Pat. No. 5,483,153 (Leeb and Kirtley) discloses a
`transient event detector` that attempts to match various transient
`basis shapes` with an observed electrical waveform to assist with
the appliance classification and identification process. However,
there is the problem of distinguishing between appliances that have
very similar characteristics with regard to consumption of
electricity, for example appliances that present substantially the
same electrical load. A particular problem is with heating
appliances which generally have a resistive heating element which
presents a purely resistive load, making it difficult to
distinguish between say a toaster and a kettle. Therefore it may
not be possible to separately totalize the power consumed by two
1200 W resistive appliances e.g. a toaster and a quartz space
heater.
[0009] As another example, Yamagami et al., "Non-Intrusive
Submetering of Residential Gas Appliances", Proceedings of the
American Council for an Energy Efficient Economy (ACEEE) Summer
Study, Pacific Grove, Calif., August 25-31, 1996, 1.265-1.273,
discloses accurately metering gas consumption in individual homes,
then analysing the data to estimate use by particular types of gas
appliance, such as cooker, stove, water heater etc. However, there
is the problem of distinguishing between appliances which have very
similar characteristics with regard to consumption of the same
utility, for example appliances which present substantially the
same electrical load.
[0010] The present invention aims to alleviate, at least partially,
one or more of the above problems.
SUMMARY OF THE INVENTION
[0011] According to a first aspect of the present invention, there
is provided a variable power load detector apparatus, for use in a
non-intrusive electrical load meter for metering the use of
electricity supplied to a plurality of loads. The apparatus
comprises an input section, a monitor section, a delta waveform
generator, an edge detector and an analysis section. The input
section is arranged to receive values representative of the total
instantaneous supply of electrical current as a function of time
from an alternating voltage supply. The monitor section is arranged
to determine current waveforms comprising sets of values
representative of the cyclic waveform of the electric current
supply. The delta waveform generator is arranged to calculate the
difference between a current waveform and an earlier current
waveform, by subtracting the respective sets of values determined
by the monitor section, to obtain a delta waveform. The edge
detector is arranged to detect an edge or edges in the delta
waveform. The analysis section is arranged to identify at least one
load based at least on information on the edge or edges detected by
the edge detector.
[0012] According to a second aspect of the present invention, there
is provided a method for detecting a variable power load, for use
in non-intrusive electrical load metering, for metering the use of
electricity supplied to a plurality of loads. The method comprises:
receiving values representative of the total instantaneous supply
of electrical current as a function of time from an alternating
voltage supply; determining current waveforms comprising sets of
values representative of the cyclic waveform of the electric
current supply; generating a delta waveform by calculating the
difference between a current waveform and an earlier current
waveform, by subtracting the respective sets of waveform values;
detecting an edge or edges in the delta waveform; and identifying
at least one load based at least on information on the detected
edge or edges.
[0013] According to a third aspect of the present invention, there
is provided an apparatus for metering the use of electricity
supplied to a plurality of appliances. The apparatus comprises an
input section, a transient detector, an analysis section, and a
processing section. The input section is arranged to receive values
representative of the total supply of electrical power as a
function of time. The transient detector is arranged to detect the
time at which an appliance is switched on from a change in the
received values due to an increase in the electric power being
supplied at that time. The analysis section is arranged to analyse
the received values and to determine: (i) a first value related to
the resistance of said appliance at the time of being switched on
detected by said transient detector; and (ii) a second value
related to the resistance of said appliance when operating in a
steady state. The processing section is arranged to identify said
appliance based on at least said first and second values, and to
determine the electrical energy consumed by said appliance.
[0014] Preferably, the analysis section is arranged to determine a
further classification value related to: the total electrical
energy supplied to the appliance from the time of switch on until
the time steady state is reached, minus the product of the steady
state power and the time from switch on until steady state is
reached; and the identification of said appliance by said
processing section is further based on said further classification
value.
[0015] According to a fourth aspect of the present invention, there
is provided an apparatus for metering the use of electricity
supplied to a plurality of appliances. The apparatus comprises an
input section, a transient detector, an analysis section, and a
processing section. The input section is arranged to receive values
representative of the total supply of electrical power as a
function of time. The transient detector is arranged to detect the
time at which an appliance is switched on from a change in the
received values due to an increase in the electric power being
supplied at that time. The analysis section is arranged to analyse
the received values and to determine: (i) the time when the
electrical power being used by the appliance has reached a steady
state; and (ii) a classification value related to: the total
electrical energy supplied to the appliance from the time of switch
on until the time steady state is reached, minus the product of the
steady state power and the time from switch on until steady state
is reached. The processing section is arranged to identify said
appliance based on at least said classification value, and to
determine the total electrical energy consumed by said
appliance.
[0016] According to a fifth aspect of the present invention, there
is provided a method for metering the use of electricity supplied
to a plurality of appliances. The method comprising: receiving
values representative of the total supply of electrical power as a
function of time; detecting the time at which an appliance is
switched on from a change in the received values due to an increase
in the electric power being supplied at that time; analysing the
received values and determining: (i) a first value related to the
resistance of said appliance at the time of being switched on; and
(ii) a second value related to the resistance of said appliance
when operating in a steady state; and identifying said appliance
based on at least said first and second values, and determining the
electric energy consumed by said appliance.
[0017] Preferably, the method comprises determining a further
classification value related to: the total electrical energy
supplied to the appliance from the time of switch on until the time
steady state is reached, minus the product of the steady state
power and the time from switch on until steady state is reached;
and wherein the identification of said appliance is further based
on said further classification value.
[0018] According to a sixth aspect of the present invention, there
is provided a method for metering the use of electricity supplied
to a plurality of appliances. The method comprises: receiving
values representative of the total supply of electrical power as a
function of time; detecting the time at which an appliance is
switched on from a change in the received values due to an increase
in the electric power being supplied at that time; analysing the
received values and determining: (i) the time when the electrical
power being used by the appliance has reached a steady state; and
(ii) a classification value related to: the total electrical energy
supplied to the appliance from the time of switch on until the time
steady state is reached, minus the product of the steady state
power and the time from switch on until steady state is reached;
and identifying said appliance based on at least said
classification value, and determining the total electrical energy
consumed by said appliance.
[0019] The present invention has the advantage of being less
computationally intensive and more accurate than previous metering
apparatus and methods.
[0020] According to a seventh aspect of the present invention,
there is provided an apparatus for metering the use of a utility.
The apparatus comprises an input section, a processor, and an
output section. The input section is arranged to receive values
representative of use of a first utility. The processor is arranged
to analyse the received values and to determine information on the
use of a second utility based on the received values. The output
section is for outputting said information.
[0021] According to an eighth aspect of the present invention,
there is provided a method for metering the use of a utility. The
method comprises: receiving values representative of use of a first
utility; analysing the received values to determine information on
the use of a second utility based on the received values; and
outputting said information.
[0022] According to a ninth aspect of the present invention, there
is further provided a computer program comprising
computer-executable code that when executed on a computer system,
causes the computer system to perform a method according to one of
the second, fifth, sixth or eighth aspects of the invention.
[0023] According to a tenth aspect of the present invention, there
is provided a computer-readable medium storing a computer program
according to the ninth aspect of the present invention.
[0024] According to an eleventh aspect of the present invention,
there is provided a computer program product comprising a signal
comprising a computer program according to the ninth aspect of the
present invention.
[0025] According to a twelfth aspect of the present invention,
there is provided an apparatus for metering the use of a utility
supplied to a plurality of appliances. The apparatus comprises an
input section, a store, and a processor. The input section is
arranged to receive, from a sensor, values representative of the
total utility being used as a function of time, and to receive time
data on the actual time of each sensed value. The store contains
appliance data characteristic of the use of the utility by each of
a plurality of appliances. The processor is arranged to analyse the
received values and time data, based on the appliance data, and to
determine information on the use of the utility by each appliance.
The output section is for outputting said information. The
processor is arranged to determine information on the use of the
utility by each appliance based on inference of the most probable
appliance or combination of appliances to be operating at a
particular time and the most probable magnitude of consumption of
the utility by each respective appliance, based on the received
values, time data and appliance data. The inference uses an
inference technique selected from Bayesian inference, neural
networks, and fuzzy logic.
[0026] Advantageously, the processor is arranged to use the
inference to calculate a probability of the plurality of appliances
being in a particular state. More advantageously, the processor is
arranged to use the inference to calculate a plurality of
probabilities, each probability being a probability that the
plurality of appliances are in a respective state; and the
processor is further arranged to determine that the plurality of
appliances are in the state with the highest probability.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] Embodiments of the invention will now be described, by way
of example only, with reference to the accompanying drawings in
which:
[0028] FIG. 1 depicts schematically a system using an apparatus for
metering the use of electricity according to an embodiment of the
invention;
[0029] FIG. 2 is a graph of voltage and current waveforms over one
cycle of an alternating supply for a TRIAC-controlled load;
[0030] FIGS. 3(a), (b) and (c) show graphs of voltage and current
waveforms for a TRIAC-controlled device, FIG. 3(a) is when the
device is turned off, FIG. 3(b) is when the device is turned on at
a power level below full power, and FIG. 3(c) shows the change in
current waveform between FIG. 3(b) and FIG. 3(a);
[0031] FIG. 4 shows graphs of voltage and current waveforms for a
TRIAC-controlled device, FIG. 4(a) is when the device is at an
initial power setting, FIG. 4(b) is when the device is turned up to
an increased power level but below full power, and FIG. 4(c) shows
the change in current waveform between FIG. 4(b) and FIG. 4(a);
[0032] FIG. 5 shows graphs of voltage and current waveforms for a
TRIAC-controlled device, FIG. 5(a) is when the device is at an
initial power setting, FIG. 5(b) is when the device is turned up to
full power, and FIG. 5(c) shows the change in current waveform
between FIG. 5(b) and FIG. 5(a);
[0033] FIG. 6 is a schematic flow chart of a method embodying the
invention;
[0034] FIG. 7 is a graph of total power supplied to an installation
as a function of time, during which an appliance is switched
on;
[0035] FIG. 8 is a graph of power consumption for two different
appliances from the time they are switched on;
[0036] FIG. 9 is a graph of resistance of two different appliances
from the time they are switched on;
[0037] FIG. 10 is a graph of power consumption for two further
different appliances from the time they are switched on;
[0038] FIG. 11 is a graph of power consumption for an appliance
from the time it is switched on until it reaches a steady operating
state;
[0039] FIG. 12 depicts schematically a system using a utility meter
apparatus according to an embodiment of the invention; and
[0040] FIG. 13 depicts schematically a system using a utility meter
apparatus according to another embodiment of the invention; and
[0041] FIGS. 14(a)-(f) show graphs of voltage and current waveforms
for a dimmer-controlled lighting system using a TRIAC semiconductor
device, FIG. 14(a) represents "off to dimmer", FIG. 14(b)
represents "dimmer to off", FIG. 14(c) represents "dimmer
increase", FIG. 14(d) represents "dimmer decrease", FIG. 14(e)
represents "dimmer to fully on", and FIG. 14(f) represents "fully
on to dimmer".
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0042] An apparatus according to a first embodiment of the
invention will now be described. FIG. 1 shows the hardware
components of a system incorporating the apparatus for metering the
use of electricity, or more correctly for metering electrical
energy. The apparatus will be referred to simply as the meter.
[0043] In FIG. 1, the electricity supply to the site, for example a
house, apartment, office, shop, school and so forth is denoted 10.
The electricity is supplied to a plurality of appliances 12A, 12B,
12C, 12 . . . by means of conventional wiring 14. The appliances
and wiring are simply shown schematically in FIG. 1, but may, of
course, be configured in any appropriate way, such as via a
consumer unit with circuit breakers or fuses, and with one or more
ring main circuits with branches or spurs. A sensor 16 is provided
to measure the total instantaneous current being provided to all of
the appliances 12 from the supply 10, and also to measure the
instantaneous voltage of the electricity supply 10. The current is
measured by any suitable sensor, for example a current clamp placed
around one of the conductors of the electricity supply wiring 14.
The current clamp typically comprises a magnetizable material, such
as ferrite, which forms a magnetic circuit around the conductor,
and acts as a transformer to induce a voltage in a secondary
winding around the magnetizable material, from which the current
flowing in the supply wiring 14 can be obtained. As an alternative
to this current-transformer, a Hall-effect sensor can be used to
measure the magnetic field in the loop of magnetizable material
around the wire which is related to the current flowing through the
wire. Other suitable ways may, of course, be used for sensing the
current.
[0044] The voltage of the electricity supply can also be measured
by any suitable volt meter. This, of course, typically requires
access to two of the conductors in the wiring 14. This can be
achieved, for example, by probes which strap around the respective
cables and have spikes which penetrate the insulation to make
contact with the conductor. Alternatively, connections could be
made to terminals in the consumer unit, or, for example, at a
location where fuses or circuit breakers are insertable.
Non-invasive capacitive voltage detectors could also be used.
[0045] As shown in FIG. 1, the sensor 16 is connected to the meter
20. It is, of course, possible that some or all of the sensor 16 is
incorporated within the meter 20, for example that wires connect
the supply wiring 14 to the meter 20, and the voltage is measured
within the meter 20. Alternatively, in a different embodiment, the
sensor 16 may be self-contained and may communicate with the meter
wirelessly, sending analogue or digital values of the instantaneous
current and instantaneous voltage. In one option, the meter 20 can
derive its own power supply by virtue of being connected to the
portion of the sensor 16 for measuring voltage. In one particular
form of this, the meter 20 is simply plugged into an electrical
outlet in the same way as an appliance 12 to obtain its power
supply and also to measure the supply voltage. However, in the
preferred embodiment, the meter 20 and sensor 16 are conveniently
located near where the utility supply 10 enters the building, such
as near where the conventional electricity meter is or would be
located.
[0046] The meter 20 comprises a number of different units, namely
an input section 22, a clock 24, a processor 26, a store or memory
28, and an output section 40. It is possible to implement each of
the various units as dedicated hard-wired electronic circuits;
however the various units do not have to be separate from each
other, and could all be integrated onto a single electronic chip
such as an Application Specific Integrated Circuit (ASIC) or Field
Programmable Gate Array (FPGA) or Digital Signal Processor (DSP)
device. Furthermore, the units can be embodied as a combination of
hardware and software, and the software can be executed by any
suitable general-purpose microprocessor, such that in one
embodiment the meter 20 could be a conventional personal computer
(PC). The software would take the form of one or more computer
programs having computer instructions which, when executed by a
computer (e.g. processor 26) carry out a method according to an
embodiment of the present invention as discussed below. The
computer programs may be stored on a computer-readable storage
medium, such as a magnetic disc, optical disc (e.g. a CD or DVD),
etc.
[0047] The input section 22 of the meter 20 receives current and
voltage values from the sensor 16. The values are input or measured
preferably multiple times per cycle of the alternating electricity
supply to a level of accuracy as required by the application. If
the values are supplied as analogue voltages, then the input
section 22 may comprise, for example, an analogue to digital
converter, such that the rest of the meter 20 can be implemented
using digital electronics. The input section 22 also receives time
data from the clock 24 which provides the actual present time. The
clock 24 could, of course, be integral with other components of the
meter, or could be part of the sensor 16, or could receive a clock
signal from an external source such as a transmitter broadcasting
time data. In one preferred embodiment the clock 24 comprises a
quartz oscillator together with other timer circuitry that is an
integral part of the processor 26 (described below). In this case,
the input section 22 for receiving the time data is also an
integral part of the processor 26. The processor performs a number
of different functions, as described below that may be referred to
by names of items, such as an edge detector and so forth; in the
preferred embodiment of the invention, these items are implemented
as software modules.
[0048] The store 28 stores a database 29 of information/data
regarding various known electrical appliances. The power
consumption of some appliances is variable. For example, a washing
machine will consume considerably different amounts of power during
different portions of a washing program/cycle and this will differ
from program to program. All such data is retained in the memory 28
for each known appliance. The store 28 may be any suitable
computer-readable storage medium, such as a solid-state computer
memory, a hard drive, or a removable disc-shaped medium in which
information is stored magnetically, optically or magneto-optically.
The store 28, may even be remote from the meter and accessible, for
example, via a telephone line or over the internet. The store 28
may be dynamically updateable, for example by downloading new
appliance data. This could be done via the supply wiring 14 itself
or, in one optional version, the store 28 is provided as an IC-card
insertable by the user into a slot in the meter 20. Manufacturers
of electrical appliances provide the necessary appliance data
either directly to the consumer, or to the utility company. New
IC-cards can be mailed to the user to update their meter 26. The
software that the processor 26 runs to perform the analysis may
also be stored in the store 28 and updated as desired in the same
ways as the appliance data (e.g. by downloading, by inserting a new
medium such as a disc or IC-card, and so on).
[0049] The processor 26 receives data from the input section 22,
the store 28 and possibly the clock 24. The processor could be a
general purpose processing device or could be a digital signal
processor or could be a bespoke hardware device (e.g. FPGA or ASIC)
manufactured specifically for implementing one or more embodiments
of the invention. The processor 26 then performs various
processing/analysis steps which are described in detail below.
Following the processing/analysis, the processor 26 produces
information regarding electrical energy utilisation for some or all
of the appliances 12. This information may be transmitted directly
to the utility provider. Alternatively, this information may be
output by the output section 40 to a user terminal 42 (such as a PC
or a dedicated device for utility-use feedback) so that the
information can be conveniently presented to the user. The user
terminal 42 can be a standard desktop or laptop computer with an
attached monitor/display 44 and/or printer 46, or can be a
dedicated device.
[0050] Although the meter 20 and the user terminal 42 are shown as
separate devices in FIG. 1, they could, of course, be part of the
same device. The output section 40 in the preferred embodiment
communicates wirelessly, for example by radio frequencies (RF)
link, or optically, or by infrared, or acoustically. However, it is
also possible that the communication with the user terminal 42 is
done through the supply wiring 14 if the user terminal 42 is
plugged into one of the supply outlets as an appliance. In a
further embodiment, the output section 40 can also act as a
receiver, such that communication between the apparatus 20 and user
terminal 42 is two-way. This enables the user terminal 42 to be
used as a further means for updating the electrical appliance data
in the store 28.
[0051] The voltage and current values together with the time data
are received by the processor 26. From the raw data, the processor
calculates a number of coefficients or signature values to
characterise the present usage. Examples of coefficients or
suitable signature values include, but are not limited to:
[0052] (a) the total real power consumption;
[0053] (b) the phase difference (angle) between the current and
voltage which depends on the load applied by the various appliances
12 and whether it is purely resistive or also reactive, i.e.
containing capacitive or inductive loads such as motors and
transformers;
[0054] (c) the root-mean-squared (RMS) current.
[0055] Clearly some of the coefficients or signature values
mentioned above are averages, typically over a minimum of one cycle
of the electricity supply, typically supplied at 50 or 60 hertz so
one cycle is approximately 0.02 seconds. However, mean values of
all of the various coefficients or signature values can be
calculated over a longer predetermined time interval. The present
values of the coefficients or signature values are compared with
the running mean value of each coefficient or signature value over
the previous cycle or cycles to obtain a change or `delta` in each
coefficient or signature value.
[0056] The processor 26 then uses inference techniques to assign a
probability to the state of all of the appliances 12 connected to
the supply 10, in terms of whether each appliance is on or off, and
the present power consumption by each appliance 12. The inference
can assign a probability to the ensemble of appliances being in any
particular state based on the calculated probability that the
appliances were in any particular state during the previous cycle
or at the previous calculation, together with the new evidence from
the changes in the various coefficients or signature values
calculated as described above, together with appliance data
obtained from a store 28 of the meter 20. The meter 20 is not
limited to knowing in advance which appliances 12 are connected to
the supply. If a new appliance is added, inferences can be made
regarding what that appliance is based on stored characteristics of
various classes of appliance.
[0057] In one preferred form, the appliance data comprises
statistical information on the probability of a specific appliance
consuming a particular amount of power. For a simple appliance,
such as a purely resistive load of an incandescent light bulb, then
the probability of it consuming a specific amount of power, when
switched on, within a small range of the nominal power, and with
negligible change in the phase angle between the current and
voltage, would be extremely high, approaching 100%. Thus if a
change in the magnitude of the power consumption equalled
approximately that value, and that light was not previously on,
then the inference would be extremely likely that the new state of
the appliances would include that light bulb being on.
[0058] In another preferred form, the appliance data stored in the
store 28 can include information such as, but not limited to,
statistical information on the probability of a specific appliance
consuming a particular amount of power, information on the time of
day, duration of use and interval between use of electrical energy
by particular appliances, information on likely groupings of
devices with increased probability of simultaneous operation, and
information on the likelihood of usage and variation in energy
consumption of appliances as a function of ambient temperature
(where ambient temperature is included as another parameter fed to
the processor).
[0059] Suitable inference techniques to perform the analysis
include, for example, probabilistic methods such as Bayesian
inference, classifiers such as neural networks, and possibilistic
methods such as fuzzy logic. Other suitable methods may of course
be used.
[0060] However, the analysis is not simply limited to monitoring
on/off events of appliances. The power consumption of some
appliances is variable. For example, a washing machine will consume
considerably different amounts of power during different portions
of a washing program and this will differ from program to program.
All these power consumptions and their probabilities for each
appliance are kept in the store 28 to enable the processor 26 to
assign a probability to the new state of all of the appliances 12,
for example using Bayes' theorem.
[0061] In this embodiment, the appliance data is in the form of a
database in which, for each appliance, a probability distribution
is stored for each of the above coefficients, for example in the
form of a probability of the appliance operating with a power
consumption within each of a plurality of ranges of power. The
statistical data to derive the probability distributions can be
obtained by a training process in which the appliance is operated a
number of times, and the mean and variance of the coefficients are
calculated. In one simple form, the appliance data for each
coefficient is a top hat distribution, centred on the mean value of
the coefficient and with a width of three times the variance of the
coefficient in question. Outside that range, the probability is
zero. Another form is a step probability distribution, for example
with three levels, highest nearest the mean and stepping down on
either side. Other distribution shapes can, of course, be used. It
is also possible that the distribution does not have a single peak,
for example in the case of an electric heater with three power
settings, there would be three peaks with low probability of power
consumption for values in between the three settings.
[0062] Naturally, the state of the appliances with the highest
probability is assumed to be the correct present state of all of
the appliances 12. A confidence-limit can also be assigned to the
present state. If a new appliance 12 is connected about which the
store 28 does not have information, then this will be picked up as
a low confidence, in which case the meter can enter a learning mode
to obtain information about the power characteristics of the new
appliance, either autonomously, or by prompting the user to input
new appliance information.
[0063] The above processing provides a first layer of analysis.
However, it may be further refined. As a second layer, the
appliance information in the store 28 also contains statistical
information on the probability of each particular appliance 12
being used at any particular time of day. This could, for example,
be expressed as a probability of a particular appliance being used
in any specific time-slot during the day, by dividing the day into,
for example, half hour intervals. This time of day probability
distribution information would be included in the database of
appliance data. Known inference principles can then also be applied
using this extra information to assign a new probability to the
state of the appliances i.e. whether any particular appliance is on
or off and the power it is consuming. Thus, for example, there
would be a low probability that particular lights were on during
the middle of the day or that a toaster was on in the middle of the
night.
[0064] A third layer of analysis can also be performed, again using
inference based on the probable duration of usage of any particular
appliance also stored as duration data as part of the appliance
data in the store 28. Thus, it would be highly probable that a
television might be in use continuously for several hours, but
improbable that a kettle would be in continuous use for more than a
few minutes. This duration probability distribution information
would be included in the database of appliance data. Using this
expected duration data, the assigned probability of the state of
the appliances can be recalculated to obtain a new highest
probability state configuration.
[0065] According to further preferred enhancement of this
embodiment of the invention, additional evidence in the form of
appliance data in the store 28 can be used to refine the state of
the appliances 12. This can include information on likely groupings
of devices, for example there would be an increased probability
that a television set and a DVD player would be operating
simultaneously, or that a computer, printer and monitor would all
be operating simultaneously. Another example would be information
on the stages of operation of an appliance, for example, during a
washing program of a washing machine, if it has previously
undergone a water-heating stage, then there would be a high
probability that the machine would then enter the next stage, such
as operating the motor to rotate the drum of the washing machine.
Optionally, the appliance data may include other characteristics,
such as data on the likelihood of the appliance being used at a
range of ambient temperatures, to capture the fact that an electric
heater is more likely to be used in cold weather, and an
air-conditioning unit in hot weather. The meter 20 can be connected
to internal and/or external temperature sensors (not shown in the
figures), and can then include ambient temperature as another
parameter in the inference of the state of the appliances in terms
of utility usage.
[0066] In the above-described embodiment, both current and voltage
of the electricity supply are measured. However, the analysis could
also be done using only the current, though with potential
reduction in accuracy.
[0067] In the detailed description below, a number of embodiments
of the invention are described. The first embodiment relates to
variable power predominantly resistive loads such as
TRIAC-controlled lighting, or similar. The second embodiment
relates to purely resistive devices with relatively constant
steady-state loads, such as heaters. The third embodiment relates
to multi-utility analysis (e.g. analysis of both electricity and
water usage). It should be noted that the three embodiments
described below may be used together or in isolation.
Variable Power Predominantly Resistive Loads (e.g. TRIAC-Controlled
Lighting)
[0068] Using the basic signature value information from the
electricity supply signals together with inference techniques can
successfully discriminate between a large number of different
appliances 12. Embodiments of the present invention are
particularly concerned with detecting variable power predominantly
resistive loads, such as TRIAC-controlled lighting, determining the
energy consumption by such loads, and tracking separately each such
load when more than one is present. The following description uses
the particular example of a dimmer switch controlling an
incandescent light. It is, of course, understood that in this
context "resistive" refers to the voltage and current flowing
through the load being substantially in phase with each other; the
load need not necessarily be ohmic nor linear. Similarly, the
invention preferably applies to detecting devices employing
intra-cycle switching to variably control the power supplied to a
load. The TRIAC is just one specific example of a controlled switch
for such devices; other examples include: SCRs (silicon-controlled
rectifiers), thyristors and transistors.
[0069] Some background to the operation of this embodiment of the
invention is described below.
[0070] A modern dimmer switch uses a TRIAC semiconductor device.
This is a non-linear device that is only turned on for a portion of
the electrical cycle. FIG. 2 shows the voltage and current
waveforms for an idealised dimmer switch controlled incandescent
light running at just over half power. The voltage waveform in FIG.
2 (and each of FIGS. 3-5) is the sinusoidal waveform and is shown
for one cycle of the alternating electricity supply. For the early
part of the cycle, no current is drawn, then at a particular point
the TRIAC is triggered and starts conducting such that there is a
step change in current flow. The current then flows (approximately
proportional to the voltage) for the remainder of the half cycle
until the voltage changes polarity (at a zero-crossing of the
voltage waveform) at which the TRIAC stops conducting. The second
half cycle is then the same as the first half cycle, just with the
opposite polarity. When the TRIAC is not conducting, no voltage is
applied across the load itself; the voltage waveform shown is that
from the supply which is applied across the TRIAC circuit driving
the load.
[0071] The point at which the TRIAC turns on can be continuously
varied, typically by adjusting a variable resistor associated with
the TRIAC circuit, generally from anywhere from the beginning to
the end of the half cycle. The point at which the conduction begins
will be referred to as a phase angle in radians in terms of the
cycle of the alternating supply, and is also called the "firing
angle". The firing angle can be anything from 0 to .pi. and in FIG.
2 it is somewhere between .pi./4 and .pi./2. By varying the firing
angle, the power consumption can be varied from substantially zero
to substantially 100% of the nominal power rating of the lighting
load.
[0072] Broadly, there are six scenarios that are of interest with
regards the state changes of dimmer controlled lighting systems.
These are:-- [0073] 1. From off to dimmer setting [0074] 2.
Increase in power to higher brightness [0075] 3. From dimmer
setting to fully on [0076] 4. From fully on to dimmer setting
[0077] 5. Decrease in power to lower brightness [0078] 6. From
dimmer setting to off
[0079] The trivial case of off to fully on (and fully on to off) is
omitted because this is already covered by methods concerned with a
purely resistive load.
[0080] Waveforms for the first three scenarios are discussed below
with reference to FIGS. 3, 4 and 5. The second three scenarios are
identical to the first three except that, the delta waveforms are
inverted. In each of FIGS. 3, 4 and 5, the first and second Figures
(a) and (b) show the waveforms before and after, respectively, the
change with which that particular scenario is concerned. The third
graph (c) in each figure is the delta waveform of the current
obtained by subtracting the current waveform (a) from the current
waveform in (b); the sinusoidal voltage waveform is shown
superimposed for reference. Of course, the waveforms shown in (a)
and (b) for each figure are idealised, and represent the current
for a single TRIAC-controlled load. In practice, many other
appliances will be operating with a significant baseload, so the
waveforms will be much more complicated, however, by subtracting to
obtain the delta waveform, the baseload is removed, and the current
change due solely to the TRIAC-controlled device is obtained.
[0081] The following description also makes use of the gradient of
the delta waveform and denotes this simply as "d/dt".
[0082] In embodiments of the invention it is necessary to detect
the sharp `turn on` and `turn off` edges of the waveform and the
deltas. The methods of detecting these will be covered in a later
section. Note that in the following text, when we refer to edges,
these are the edges in the first half of the cycle. For every edge
in the first cycle, there will be a corresponding edge in the
second half of the cycle which is of opposite polarity.
1) A TRIAC Type Device Turns on--FIG. 3.
[0083] In this case, the delta waveform FIG. 3(c) shows a single
edge with d/dt>0. This is at firing angle .alpha..sub.on.
[0084] The change in real power is positive.
2) A TRIAC Type Device Increases in Power Consumption--FIG. 4.
[0085] In this case, the delta waveform FIG. 4(c) shows two edges.
The first has d/dt>0 and is at .alpha..sub.on. The second has
d/dt<0. Note that the position of this edge is identical to the
`.alpha..sub.on` from section one.
[0086] The change in real power is positive.
3) A TRIAC Type Device Increases in Power Consumption to Fully
on--FIG. 5.
[0087] In this case, the delta waveform shows a single edge with
d/dt<0. The change in real power is positive.
TABLE-US-00001 TABLE 1 Summary of the Scenarios First Second
Waveform Edge Edge Change in Scenario Delta d/dt d/dt Real Power 1.
Off to dimmer See FIG. 14(a) >0 N/A +ve 2. Dimmer to off See
FIG. 14(b) <0 N/A -ve 3. Dimmer increase See FIG. 14(c) >0
<0 +ve 4. Dimmer decrease See FIG. 14(d) <0 >0 -ve 5.
Dimmer to fully on See FIG. 14(e) <0 N/A +ve 6. Fully on to See
FIG. 14(f) >0 N/A -ve dimmer
[0088] Thus it can be seen that by considering the number of edges
in the delta waveform, the polarity of those edges and the change
in real power associated with the delta, that we can fully detect
each scenario.
[0089] Identification of the device which has changed power
consumption is described below.
[0090] In most domestic residences, there are multiple loads
controlled by dimmer switches and each load can have a different
full power. For an advanced and accurate NIALM system, it is
necessary to not only apportion the change in power to a class of
loads `lighting` but in actual fact to track the power consumption
of individual loads.
[0091] Following the detection of a TRIAC event, the first stage is
to calculate the full power load of that device. By doing this, we
can identify the difference between say a 100 W load @ 30% power
compared to a 60 W load @ 50% power.
[0092] To do this is non-trivial. One method is to calculate the
power consumption of the device by the integral of the power and
hence relate the total power consumption to the firing angle of the
TRIAC . . . .
P = 1 2 .pi. .intg. 0 2 .pi. V I ( .omega. t ) = 1 2 .pi. .intg.
.alpha. .beta. v 0 sin ( .omega. t ) i 0 sin ( .omega. t ) (
.omega. t ) ##EQU00001##
where .alpha. is the turn on point and .beta. is the turn off
point.
[0093] However, this may be wildly inaccurate in some circumstances
on account of the non-linearity of the load with firing angle.
[0094] Instead, a preferred way is to calculate the `effective
voltage` that the load sees.
[0095] The effective RMS voltage as seen by the TRIAC-controlled
load can be calculated as follows
( V RMS - E ) 2 = 1 2 .pi. .intg. .alpha. .beta. v 0 2 sin 2 (
.omega. t ) ( .omega. t ) ##EQU00002##
Which leads to a solution . . .
( V RMS - E ) 2 = v 0 2 2 .pi. { .pi. - .alpha. + 0.5 sin ( 2
.alpha. ) } ( 1 ) ##EQU00003##
when .beta.=.pi.; this corresponds to the case when we turn a TRIAC
on. (i.e. it was previously off.)
[0096] .lamda..sub.0 is the peak of the voltage supply (equal to
the actual RMS line voltage.times. {square root over (2)}).
[0097] The power consumed by a perfectly resistive load is
proportional to the square of the rms voltage--the constant of
proportionality being 1/resistance. In actual fact, in the case of
an incandescent bulb, the power is proportional to the rmsVoltage
to the power approximately 1.5.
[0098] Knowing this relationship, it is thus possible, given the
power of a light and the effective RMS voltage applied to calculate
the power that would be consumed at the nominal line voltage (240V
RMS in the UK) according to the following formula . . .
P Norm = P Observed .times. V RMS - LINE .gamma. V RMS - E .gamma.
( 2 ) ##EQU00004##
[0099] where .gamma. is approximately equal to 1.5 and
V.sub.RMS-LINE is the RMS line voltage of the supply (nominally
240V in the UK.)
[0100] Thus, when a TRIAC turns on (scenario 1 above), we know the
peak voltage, the firing angle (.alpha.) and P.sub.Observed (which
is the power delta) and thus can work out the nominal power of the
load.
[0101] This is useful in the two cases where we turn the TRIAC on
and off, but we can generalise further . . . .
[0102] Consider that we turn on a TRIAC controlled load, with
firing angle .alpha..sub.1 and power change .DELTA.P.sub.1. Using
this information, we can calculate the effective RMS voltage and
thus the nominal power of the load.
[0103] Rearranging equation (2) above leads to
.DELTA. P 1 = V .alpha. 1 .gamma. V RMS - LINE .gamma. .times. P
Norm ( 3 ) ##EQU00005##
where V.sub..alpha.1 is the effective RMS voltage delivered when
the firing angle is .alpha.1.
[0104] We now increase the power to the load by decreasing the
TRIAC's firing angle. The observed power change is .DELTA.P.sub.2,
the firing angle is .alpha..sub.2 and the total power being
delivered to the load is (.DELTA.P.sub.2+.DELTA.P.sub.1)
[0105] We can calculate the effective RMS voltage as seen by the
load and this is denoted V.sub..alpha.2. Substituting into equation
(2) above gives
P Norm = ( .DELTA. P 1 + .DELTA. P 2 ) .times. V RMS - LINE .gamma.
V .alpha. 2 .gamma. ( 4 ) ##EQU00006##
[0106] Substituting in equation (4) above and rearranging gives the
expression
P Norm = .DELTA. P 2 .times. V RMS - LINE .gamma. V .alpha. 2
.gamma. - V .alpha. 1 .gamma. ( 5 ) ##EQU00007##
[0107] Thus for any change in state of the TRIAC, we can calculate
the nominal power of the load and thus identify the load being
controlled. Corresponding expressions can be derived for loads
controlled by SCRs or other types of controlled switches.
[0108] (Note, when turning a TRIAC on, .DELTA.P.sub.2 is the
observed power change, and V.sub..alpha.1 is zero.)
[0109] In addition to calculating a value for P.sub.Norm, equation
5 can also be used to derive the value of .gamma.. Following the
change in state of a triac-controlled load the values of .alpha.1
and .alpha.2 and .DELTA.P.sub.2 are known. These can then be stored
awaiting a further change in state of this load. Following a
further change in state of this load, one can solve for .gamma.
since P.sub.Norm will be the same in both cases. Thus .gamma. is
the only unknown and can be solved by conventional mathematical
techniques. It is of course possible to calculate .gamma. from
multiple data points to further increase accuracy. One can
simultaneously solve for P.sub.Norm using equation (5) and thus use
P.sub.Norm to match up state changes to the same appliance. In the
event that P.sub.Norm is unknown, one can instead match up unknown
events to an appliance based on prior knowledge of the state of
that appliance, for example from the firing angle.
[0110] Additionally, in certain cases where there are multiple
loads under TRIAC control, one can further aid identification by
considering the positions of the edges. A summary of methods for
identifying the specific load following a TRIAC event are shown
below in Table 2.
TABLE-US-00002 TABLE 2 Scenario Waveform Delta Methods for
identification of device 1. Off to See FIG. 14(a) Calculate the
nominal power of the load based on dimmer firing angle. The load
must have been previously off. 2. Dimmer to See FIG. 14(b)
Calculate the nominal power of the load based on off firing angle.
In the case that there are multiple loads of this power on, can
identify specific load by matching the position of the edge with
the last known firing angle. 3. Dimmer See FIG. 14(c) Calculate the
nominal power of the load based on increase firing angle. In the
case that there are multiple loads of this power on, can identify
specific load by matching the position of the -ve edge with the
last known firing angles. The new firing angle is given by the
position of the +ve edge. 4. Dimmer See FIG. 14(d) Calculate the
nominal power of the load based on decrease firing angle. In the
case that there are multiple loads of this power on, can identify
specific load by matching the position of the -ve edge with the
last known firing angles. The new firing angle is given by the
position of the +ve edge. 5. Dimmer to See FIG. 14(e) Calculate the
nominal power of the load based on fully on firing angle. In the
case that there are multiple loads of this power on, can identify
specific load by matching the position of the -ve edge with the
last known firing angles. The new firing angle is 0 degrees once
the dimmer is fully on. 6. Fully on to See FIG. 14(f) Calculate the
nominal power of the load based on the dimmer firing angle. In the
case that there are multiple loads of this power, one can restrict
the search to devices which are known to be fully on.
[0111] Edge Detection is Described in Detail Below.
[0112] As is apparent, it is necessary to accurately detect the
edges in the signal. There is a vast body of literature available
with regards edge detection algorithms. Any of these known edge
detection algorithms may be used to detect edge(s) in the delta
waveform described above. However, accuracy can be improved in this
system through three mechanisms . . . .
[0113] 1: By employing a sensor/pre-processing stage that removes
DC offset from the current signal, we can be assured that prior to
a positive edge, the average value of the current delta must be
zero, or close to zero such that the magnitude of the difference
from zero is below a threshold value (i.e. approximately zero).
Similarly, following a negative edge, the average value of the
current delta must be approximately zero. These statements apply
when either: (i) the edge is in the first half of the waveform and
the change in real power is positive; or (ii) the edge is in the
second half of the waveform and the change in real power is
negative. For the cases of (iii) an edge in the first half of the
waveform and a negative change in real power or (iv) the edge in
the second half of the waveform and a positive change in real
power, then a positive edge should be followed by an approximately
zero value of the current delta, and a negative edge should be
preceded by an approximately zero value of the current delta. Thus,
by measuring the current delta value either side of the edge, one
can improve the accuracy of detected edges and thus reject noise
and other signal artefacts that would otherwise be mis-identified
as a TRIAC turning on.
[0114] 2: One can assume that as long as the total power measured
by the monitor is stable, then the position of the edges are
constant. Edges due to noise etc. will not be constant assuming
random noise patterns and hence once can average the results over
multiple cycles to improve accuracy, either by taking an average of
the current delta waveform, or alternatively by running the edge
detection algorithm and averaging the results.
[0115] 3: Assuming that there is no DC offset, the system should
exhibit symmetry--for every edge .alpha.1 measured in the first
half of the cycle (0<.alpha.1<.pi.), there will be another
edge .alpha.2 in the second half of the cycle .alpha.2, where
.pi.<.alpha.2<2.pi. and .alpha.2-.pi.=.alpha.1.
[0116] In reality, it is possible that .alpha.2-.pi. does not
exactly equal .alpha.1 due to imperfections in the system and the
devices, hence, the algorithm should be tolerant to small
deviations.
[0117] There are other techniques which could also be applied to
complement or replace the standard (albeit improved) methods of
edge detection described above. One such alternative or additional
edge detection technique is described below, in which the first
`turn on` edge is referred to as a and the second `turn off` edge
is referred to as .gamma..
[0118] As mentioned above, TRIACS are used to provide variable
power control for electrical systems. They work by gating the
voltage supplied to the appliance between the start of the
electrical cycle (0.degree.) and a variable point between 0.degree.
and 180.degree. degrees in that cycle, with 0.degree. corresponding
to full power and 180.degree. corresponding to zero power. As
described above, the characteristic `gated sine wave` pattern in
the current waveform is detected indirectly by looking for edges.
However, one could also look for the characteristic `gated sine
wave` pattern by quantifying a measure of similarity between the
waveform under analysis and a reference waveform. One such measure
of similarity could be the correlation coefficient which measures
the strength of a linear relationship between two variables.
[0119] Thus, one possible approach would be to create a set of
basis waveforms consisting of a set of gated sine waves comprising
every possible value of .alpha. and .gamma.. For a waveform
comprising N points, this set would have N.sup.2 members.
Correlations could then be performed between the candidate waveform
delta and each of our basis waveforms and the highest correlation
would correspond to the closest match.
[0120] Since the delta waveform may be caused by a non-TRIAC
device, it would be advantageous to have a "reject" option where,
if the correlation coefficient was not high enough, then such an
event would be rejected as a possible TRIAC event.
[0121] In addition, the correlation coefficient should preferably
be modified by a term relating to the size of the correlation
window (.alpha.-.gamma.) since the smaller the window, the more
likely a high correlation coefficient due to random noise. A
possibly modification term would be {square root over (N)} though
any function that increases with N could be used.
[0122] It would also be possible to correlate with other gated
functions. The most appropriate for a resistive device being
controlled by a TRIAC would be a gated voltage waveform since, for
a purely resistive device, the current drawn by that device is
directly proportional to the supply voltage. Since the supply
voltage is often not sinusoidal, such a technique can yield better
results.
[0123] The correlation operation is of order N which means that, in
order to correlate against the full set of basis waveforms, the
computation time is of order N.sup.3. It would be possible to
reduce the computation time (and therefore speed up the system
somewhat) by reducing the search space. For example, there is a
constraint that .alpha. is less than .gamma., which can reduce the
search space by half. Moreover, one could run a more standard edge
detection algorithm (as referred to above) in conjunction with this
correlation technique for detecting the edges. By identifying
candidate edges, one can vastly reduce the search space to look
over. Advantageously, by using this correlation technique in
combination with the edge detection described above, one can
improve the resilience of the system to rogue edges which may occur
in a noisy system.
[0124] The Operation of an Embodiment of the Invention is Described
Below.
[0125] The following processes, described with reference to the
flow chart of FIG. 6, are carried out by one example of an
apparatus embodying the invention. The processes may be performed
by the general processor 26 for example as software modules, or may
be implemented in hard-wired dedicated hardware.
[0126] Measure signature values of interest (based on instantaneous
current and voltage values received at an input section, step S10)
at a pre-determined rate (at a rate of every cycle, or slower. One
could also average over multiple cycles. To date, Real Power has
proved to be the most reliable signature, but there are others) to
monitor whether the background load is `stable` (i.e. inter-cycle
variation in the measured signatures is below some pre-determined
power). If the signature is deemed to be stable, then it can be
assumed that there has been no change in the power signatures as
drawn by all appliances on the supply and this stable signature is
recorded along with the current waveform.
[0127] If there has been a change in signature(s), then we assume
that an appliance has changed the amount of power that it is
drawing. A change in signature (such as the amount of power) is
detected by an event detector in step S20. We may then run multiple
analyses designed to detect specific appliance classes and compare
the results from each classifier to identify which appliance has
changed state. The following describes, by way of example, a
classifier to detect variable power loads, such as lighting
circuits.
[0128] A monitor section determines current waveforms in step S30.
The `stable` current waveform can be a single waveform preceding
the detected event or can be a weighted mean of preceding
waveforms. The weighted mean can be a simple average (i.e. all
weights equal 1) or can place greater weight for example on more
recent samples. The waveform after the detected event can also
either be a single waveform or a weighted average. In step S40 a
delta waveform generator then calculates the `current delta`
waveform by subtracting each sample of the `stable` current
waveform from the present current waveform (after the detected
event).
[0129] An edge detector then analyses the delta waveform to look
for edges in step S50. A simple method would be to threshold on
d/dt. A more advanced method looks for areas of local maximum in
d/dt (i.e. the differential at a sample is greater than the samples
either side) or by looking at zero crossings in the second
derivative. For more details, reference `Edge Detection
Techniques--An Overview.` By Ziou and Tabbone. To improve the
detection of edges, the waveform may first be filtered to remove
noise.
[0130] If one or more edges are detected, two further checks can be
made.
[0131] 1: If d/dt is positive, then the delta current level between
the zero crossing preceding the edge of the waveform and the edge
should be approximately zero. This can be calculated by numerous
methods:--e.g. one could check that the magnitude of each sample is
below the maximum noise level of the system. Alternatively, one
could average or integrate the current prior to the edge and check
that this is below the expected noise level.
[0132] Similarly, if d/dt is negative, then the current following
the edge should be approximately zero.
[0133] As explained previously, and as is apparent from Table 1,
these statements apply to edges in the first half of the waveform
for situations in which the change in real power is positive
(scenarios 1, 3 and 5). The polarity of the edges should be
reversed for scenarios 2, 4, 6, and reversed (again) for edges in
the second half of the waveform.
[0134] 2: To improve accuracy, one can look over multiple cycles.
As long as the signal is stable, then the edges should remain in
the same position from cycle to cycle. Thus, one can remove false
edge detections by looking over multiple cycles.
[0135] Once it has been confirmed that one or more edges has been
detected, an analysis section then consults Table 1 to work out
which one of the six scenarios is occurring, based on the number of
edges, the order of the edges and the real power delta (change in
real power).
[0136] Finally, in step S60, the analysis section can now identify
the specific load that has changed state.
[0137] One can calculate the effective RMS voltage based on the two
firing angles, using equation (1) above. Alternatively, one could
use a look up table if so desired to ease computation at the
expense of memory. Finally, by substituting the effective RMS into
equation (5), one can calculate the nominal full power of the
load.
[0138] Knowing the full power of the load allows us to identify the
specific appliance /class of appliance that has changed state--e.g.
100 W light bulb on dimmer. Secondly, one can iterate through each
appliance of that class that is currently known (in the data store
28) to establish which of those could have changed to the new
measured state, based on it's current state. For example, suppose
that we have identified that we are in `scenario 3--increase in
power.` That means that the only appliances that can have changed
state are those that are currently in a dimmer mode. Finally, one
can match up the firing angles to identify the specific
appliance--e.g. if in scenario 3, then the -ve d/dt edge on the new
waveform must match up with the current +ve d/dt edge of the
appliance which has changed state. Further information on
disambiguating identified appliances is given in Table 2.
[0139] It is likely that having iterated through the algorithm,
there may be more than one contender, each with a measured
`likelihood,` which may be a probability, or may be a possibility
measure. These may be then combined with the scores from other
classifiers using a master classifier, which may be (but not
exclusively) for example a Bayesian engine, or a Neural Net. If no
match with a known appliance is found, then a new appliance entry
can be made in the data store 28 for future use.
[0140] Following the analysis, in this example, the processor
produces a log of the electrical energy utilisation for each
appliance (step S70 of FIG. 6), comprising total energy
consumption, time of day and duration of each usage. This
information is output by the output section 40 to the user terminal
42 (such as a PC or a dedicated device for utility-use feedback) so
that the information can be conveniently presented to the user.
Purely Resistive Devices with Relatively Constant Steady-State
Loads (e.g. Heaters
[0141] Using the basic coefficient information from the electricity
supply signals together with inference techniques can successfully
discriminate between a large number of different appliances 12.
However, there can still be a problem with distinguishing between
appliances with similar electrical characteristics, for example,
those which present essentially a purely resistive load and have a
heating function, such as space heaters, kettles, toasters, irons,
cooking hobs, ovens, tumble dryer heating elements, water heaters
and so on. These loads are purely resistive, so there is no phase
angle information between the current and voltage to distinguish
between them, and there is a considerable overlap in the magnitude
of the power consumption of different appliances within this class.
The present invention is particularly concerned with discriminating
between these appliances. As will be explained below, the invention
can also be used to assist in determining what type of appliance
each unknown resistive appliance is likely to be, for example to
identify that a particular unknown appliance is a kettle. This
information can then be used to identify the kettle in future with
a higher degree of accuracy.
[0142] FIG. 7 is a graph of electrical power consumption (vertical
axis) as a function of time (horizontal axis) for a particular
installation, such as a house. At time t.sub.0 a further appliance
is switched on and the power consumption rises extremely rapidly to
a peak. The power consumption then falls more slowly to a steady
state value. The processor 26 analyses the total power consumption
using a transient detector circuit or software module to detect
such a large increase in power consumption, for example in excess
of 50 watts over one cycle of the alternating current electrical
supply and then monitors the power until the magnitude of the
gradient (rate of change of power with respect to time) is below a
predetermined threshold and identifies that as the time t.sub.1 at
which a new steady state has been reached. The time from t.sub.0 to
t.sub.1 is denoted t.sub.ss in FIG. 7, i.e. t.sub.ss is the time
from switching on the new appliance until a substantially steady
state has been reached.
[0143] The shaded area in FIG. 7 represents the base load, i.e. the
power consumption by other appliances. This is subtracted from the
power values plotted in FIG. 7 to obtain the power consumed by only
the appliance that is switched on at t.sub.0. One method is to take
the base load power as being the measured power immediately prior
to t.sub.0 and subtract this from each subsequent power value. This
assumes the base load is constant. One way to account for a varying
base load is to calculate the mean and variation in the base load
over a longer period of operation and thus obtain a representative
average power which is then subtracted from the measured power. A
further possibility is to measure the base load mean and variation
also after the further appliance has switched off (by detecting the
switch off event) and if this is different from the base load prior
to the appliance being switched on, then a linear variation in base
load between the on and off events of the appliance under
observation can be assumed and accordingly subtracted from the
measured total power to obtain the power consumption of the
appliance in question.
[0144] FIG. 8 shows the power consumption for two different
appliances after the base load has been subtracted. FIG. 9 is a
plot of the corresponding resistance of the appliances which can be
obtained by dividing the voltage by the current (for example RMS
values over one cycle in each case), or can be obtained by dividing
the power by the square of the RMS current. In FIGS. 8 and 9
t.sub.0 is at cycle number 0 of the plot, and clearly t.sub.1 is
significantly different for appliance 1 and appliance 2.
[0145] The physical process underlying these graphs is that the
resistance of a heating element varies as a function of
temperature. When the appliance is switched on, the resistance has
a value R.sub.0 at time t.sub.0. The element then heats up which
increases its resistance until it reaches a maximum value R.sub.1
at time t.sub.1. This occurs when the appliance has reached its
steady state operating temperature. This is an equilibrium at which
the rate of electrical energy input to the heating element is
balanced by the rate of cooling of the element (by conduction,
radiation and convection). As can be seen from FIG. 8, both
appliances have a steady state power of approximately 2.4 kilowatts
and so would be indistinguishable by that parameter alone. However,
appliance 1 has much lower heat losses than appliance 2, and so has
a higher operating temperature for its heating element and
therefore greater change in resistance from its value when cold
before being switched on.
[0146] The resistance of a heating element is related to its
temperature as follows:
.DELTA. R R 0 = .alpha. .DELTA. T ( 6 ) ##EQU00008##
where R.sub.0 is the initial resistance of the element, .DELTA.R is
the change in resistance as the element heats up, a is the
temperature coefficient of resistivity, and .DELTA.T is the change
in temperature of the element.
[0147] In one embodiment of the invention, one coefficient or
parameter (also called a classification value) that characterizes
the electrical appliance is the ratio .DELTA.R/R.sub.0, where
.DELTA.R=R.sub.1-R.sub.0, i.e. the difference in resistance at
between times t.sub.1 and t.sub.0. For appliance 1 and appliance 2
whose electrical characteristics are given in FIGS. 8 and 9, the
value of this ratio is given in Table 3 below.
TABLE-US-00003 TABLE 3 Appliance .DELTA.R/R.sub.0 Appliance 1
0.2061 Appliance 2 0.0138
[0148] Clearly this resistance ratio (fractional change in
resistance) can be used to distinguish between appliances that have
very similar steady state power consumption. Values of this ratio
for different appliances or classes of appliances can be kept in
the store 28. Then when an appliance is switched on and a value of
the resistance ratio obtained, that value can be included in the
inference calculation, along with the other coefficients discussed
elsewhere, to produce the most probable estimate of which
appliances are on any particular time, and the energy consumption
by each appliance. Even when a new appliance is used for the first
time, the resistance ratio can be used to identify the class of the
appliance (e.g. whether it is a toaster or a kettle) from known
values of such appliances in general, without any a priori
knowledge of the new appliance itself.
[0149] In a further enhancement, one can substitute a value for the
temperature coefficient of resistivity .alpha. in Equation (6) and
then solve directly for the temperature change .DELTA.T to assist
in identifying the appliance. The value of a for nichrome could be
assumed because that is the most common heating element material.
In fact one can iteratively solve for .DELTA.T using various common
values for a to further increase confidence in the identification
of an appliance. For example, having detected what appears to be a
light bulb, one could substitute in the value of a for tungsten (as
used for incandescent light bulb filaments) and solve for .DELTA.T
in Equation (6). If the resulting .DELTA.T is around 3000 K, then
this supports the inference that the appliance is a light bulb. If
.DELTA.T is 100 K however, then the likelihood is that it is not a
light bulb.
[0150] Although the specific embodiment described above envisages
using the value of the ratio .DELTA.R/R.sub.0 as being
characteristic of specific appliances, this is not essential.
Ratios of other quantities such as power or current could equally
well be used; they are both related to the value of the resistance,
which is what is fundamentally physically changing as the heating
element of the appliance reaches its operating temperature.
Furthermore, in the preferred embodiment, in FIG. 7, the power
plotted was the product of the current and voltage; however a
simplifying assumption could be made that the voltage is
substantially constant and so the power is just directly
proportional to the current, and the resistance of the appliance is
simply inversely proportional to the current through that
appliance, and therefore it is not essential to measure the
voltage. A further possibility when considering the power used by
an appliance is to base the calculation on the power in the first
harmonic of the alternating supply rather than the total power.
[0151] Another alternative is that, instead of determining the time
t.sub.1 when the current, power or resistance reaches a steady
state, the apparatus simply detects when the appliance switches off
and measures the resistance at that point and uses the "switch-off"
resistance in place of R.sub.1 when calculating .DELTA.R (or
equivalently measures the switch-off current or switch-off power
from the from the change in electrical parameters when the
appliance switches off and uses those in obtaining mathematically
equivalent ratios as the relevant classification values).
[0152] FIG. 10 shows the power profiles for two further appliances
which have the same nominal (steady state) power and cooling rates,
but still have different profiles. These two appliances could not
be distinguished on the basis of steady state power consumption or
fractional change in resistance because those values are the same.
The difference between these appliances is the thermal mass of the
heating element and any other parts of the appliance that are being
heated. For example, the effective thermal mass of an electric iron
which has a large heating plate heating element is much greater
than that of, for example, a toaster, which has a small heating
element. Again, looking at it physically, the heat energy added to
a system is related to the change in temperature of that system as
follows:
Q=cm.DELTA.T (7)
[0153] where Q is the net heat energy added to the system, c is the
specific heat capacity of that system, m is the mass of the
material heated, and .DELTA.T is the change in temperature of the
system.
[0154] In the case of an electrical appliance, the net heat energy
added, Q, is equal to the integral with respect to time of the
power supplied minus the heat lost through all cooling mechanisms.
Although the electrical energy supplied can be obtained by
integrating the electrical power from the electrical measurements,
and .DELTA.T can be approximated from Equation (6) from the
resistance ratio and by assuming a value for .alpha. from known
material properties (most heating elements are nichrome), the
product cm cannot be solved because the heat losses from the
appliance are unknown and would generally be difficult to model
because of their dependence on complex processes and their
variation as a function of temperature.
[0155] However, according to a further embodiment of the invention,
a classification value that is related to the thermal mass (cm) and
serves as a suitable classification value with which to
discriminate between appliances has been found. This can be used
both to identify the type of appliance (e.g. whether it is a
toaster or a kettle) and to act as a further classification value
to detect every time that particular appliance is turned on.
Referring to FIG. 11, this shows the electrical power drawn by an
appliance from the time of switch on t.sub.0 to the time t.sub.1 at
which a steady state is reached, this total time being t.sub.ss.
Although plotted in terms of number of cycles, the physical time in
seconds is directly proportional to the number of cycles of the
alternating supply. The dark-shaded, approximately triangular area
at the top of the graph is the area of interest according to this
embodiment of the invention. This area provides a signature or
classification value that can conveniently be used to distinguish
between appliances, even when their steady-state power consumption
P.sub.1 is substantially the same. Crudely speaking, the
dark-shaded area is related to the excess energy that must be
supplied to the appliance over and above the energy required to
maintain a steady state when the appliance is at its operating
temperature. This area is related to the energy required to heat up
the appliance, which in turn is related to the thermal mass of the
system (product of the physical mass and the specific heat
capacity).
[0156] One way to calculate this area is as follows: following the
detection of an "on event" for a resistive appliance by the
transient detector, the processor 26 starts to integrate the power
with respect to time. When the power has stabilised (reached a
steady state at which its gradient is below a threshold value),
integration is stopped. The integral value obtained gives the total
area under the curve. The lower rectangular area is calculated by
multiplying the final steady-state power P.sub.1 by the integration
time (t.sub.ss) and this is then subtracted from the total integral
value to obtain the dark-shaded area of interest. This area is then
used as a classification value along with some or all of the other
values and coefficients defined above to determine which appliance
was switched on and ultimately to calculate the energy consumed by
that appliance.
[0157] The above example is simply one way of calculating the area
to obtain a classification value. Other methods could equally be
used, for example by approximating the shape as a triangle and
calculating the area as:
1 2 t ss ( P 0 - P 1 ) ##EQU00009##
or by other methods that seek to approximate the shape of the
transient by fitting an approximate curve, and then integrating
that, or other numerical integration techniques.
[0158] Furthermore, although in the above description the
integration to find the area as a classification value is carried
out on the real power, it could alternatively be carried out on the
power in the first harmonic, or the power where the voltage is
assumed to be constant (so the power is just related to the
current, and would be equivalent to integrating the RMS current),
or other derivations, such as equivalent areas on the resistance or
impedance curves which are mathematically equivalent and related to
the electrical energy supplied to the appliance in excess of that
required to maintain the steady state.
[0159] Another classification value that can be used in the
inference by the processor 26 is the time t.sub.ss until steady
state is reached, and also the steady state power itself. These,
together with the other appliance characteristics such as time of
day of use, duration of use, frequency of use and so on enable
appliances with resistive loads to be uniquely identified.
[0160] It is not necessary to calculate the full set of
classification values or appliance signatures every time that the
appliance turns on, and indeed this may not always be possible if a
further appliance is turned on while the first appliance is still
warming up, such that the two transients overlap. However, the
switch on power (P.sub.0) and the steady state power (P.sub.1 or
equivalently the power at switch-off) can be measured in a single
cycle of the alternating electricity supply independently of the
base load or other appliance transients. Therefore, in these
circumstances, these power levels can be assigned as belonging to
"appliance A". In future, once the full transient information has
been measured and the appliance has been classified, then a search
through the local database will show that appliance A is in fact,
for example, a kettle, and the energy consumption information can
be updated accordingly. This inference can be reliable because in a
typical house there are only a relatively small number of
appliances of any particular type, and the set of appliances in the
house does not change frequently. Therefore, once it has been
established that a particular appliance is present, then it would
be very unlikely that there is another appliance in the house that
has exactly the same switch on power and steady state power
levels.
[0161] A further enhancement is to take into account the cooling
down of a resistive appliance after it turns off (either as a
result of the natural end of its cycle of use, or as part of a
thermostatic control). As it cools, its resistance will decrease.
Then if it turns on again, before it has completely cooled down to
ambient temperature, the measured resistance will be somewhere
between the normal operating resistance R.sub.1 and the cold
resistance R.sub.0. By monitoring appliances over time, the
processor 26 will be able to deduce the rate of cooling and thus,
when a switch on transient event is detected, be able to determine
whether this is a new appliance switching on, or whether it is the
previous warm appliance switching on again, based on the time since
that appliance last switched off.
[0162] Following the analysis, in this example, the processor
produces a log of the electrical energy utilisation for each
appliance, comprising total energy consumption, time of day and
duration of each usage. This information is output by the output
section 40 to the user terminal 42 (such as a PC or a dedicated
device for utility-use feedback) so that the information can be
conveniently presented to the user.
[0163] According to a further embodiment of the invention, one or
more of the appliances 12 connected to the supply wiring 14 can be
a generator of electrical power, for example a solar photovoltaic
panel or a wind turbine generator. As these devices generate power,
which is either fed to other appliances 12, or even back to the
supply utility 10, then the current and voltage detected by the
sensor 16 would also change, and the processor 26 can perform
exactly the same analysis based on appliance data stored in the
store 28 to determine when each device is generating power and the
quantity generated. This gives convenient feedback about the
precise savings achieved by using the solar panel or wind turbine,
and also information about optimal siting of such devices.
[0164] In the embodiments of the invention described in this
section ("Purely resistive devices with relatively constant
steady-state loads") and the previous section ("Variable power
predominantly resistive loads"), only electrical energy is measured
and discussed. However, the meter could be concerned with two or
more utilities, for example additionally measuring water and/or gas
consumption to improve inference of which appliances are in use at
a particular time; in general the meter may aggregate information
about multiple utilities to improve confidence in the inferred
usage (for example by particular appliances) of each one of the
utilities. This idea is explored further in the "Multi-utility
analysis" section below.
Multi-Utility Analysis (e.g. Analysis of Both Electricity and Water
Usage)
[0165] An apparatus, referred to as a utility meter, according to
an embodiment of the invention will now be described. FIG. 12 shows
the hardware components of a system incorporating the utility meter
20. In this embodiment, the invention is applied to an electricity
supply system, by way of example, and so the utility in question is
electricity, or more correctly, electrical energy. Many of the
components shown in FIG. 12 are similar to those shown in FIG. 1.
Therefore, these will not be described again here since the same
reference numerals have been used in each case.
[0166] Using the intrinsic information in the electricity supply
signals together with inference techniques can successfully
discriminate between a large number of different appliances 12.
However, there can still be a problem with distinguishing between
appliances with similar electrical characteristics. For example,
consider an electric room heater and a so-called "power shower"
(which uses electricity to instantaneously heat water for a shower)
of the same power rating in terms of kiloWatts; both are
essentially purely resistive loads and draw the same current.
Similarly, consider a washing machine and a tumble dryer; each has
a resistive heating element and an electric motor for rotating a
drum under a similar load. The present invention uses information
on the use of another utility to assist in distinguishing between
use by such similar appliances, or to increase the confidence that
the correct inference has been made regarding the state of the
appliances, as will now be described.
[0167] In the embodiment of the invention shown in FIG. 12, the
appliance 12A, such as a washing machine or power-shower, is
connected to the supply 30 of another utility, in this case water.
A water meter 32 detects the flow of water and conveys values
representative of use of water to the input section 22 of the
utility meter 20. These values are used in the inference performed
by the processor 26, in conjunction with known characteristics of
the appliances 12 read from store 28, and the electrical
information as already described above, to generate an improved
inference of the state of the appliances 12, or an inference with
greater confidence that the assessment is correct. For example, if
it is detected from the current measurements that an electrical
appliance is consuming a particular amount of power, and
simultaneously there is a flow of water corresponding to that of a
power-shower, then the probability is high that the electrical
power is being supplied to a power-shower. Conversely, if the same
electrical power consumption by an appliance is determined, but in
the absence of the water flow, then the inference will be that a
different electric heater is in use.
[0168] Although not shown in FIG. 12, the water can be supplied to
multiple appliances, some of which also use electricity, and some
of which do not use electricity. By including the water usage
information in the inference analysis, the state of the electrical
appliances can be derived with greater accuracy (for example
because different appliances uses different flow rates of water,
and some none at all, and such characteristic data is included in
the store 28). Similarly, the inference can be performed the
opposite way round such that the knowledge of electricity usage can
enable or improve determination of which appliances are using
water. Effectively the available utility usage information is
aggregated, and used in the overall inference of the present state
of all appliances, and can be used to refine the previous estimates
of the past states of the appliances. In this way, the utility
meter 20 can act as a combined meter for multiple utilities.
[0169] The invention is not limited to the utilities comprising the
pair of water and electricity. For example, gas and electricity
could be monitored. If it is inferred from gas flow data that a gas
hob is being used, and also that some electric appliances are
switched on, then it would be more probable that the electric
appliances are associated with the kitchen, for example an
extractor hood or kitchen light, rather than say a bathroom
extractor fan or light. In this way the confidence of the
assessment of which appliances are in use can be improved. The
general principle is that values representing the use of a first
utility, such as water or gas, are used to determine information on
the usage of a second utility, such as electricity, or vice
versa.
[0170] The stored appliance characteristics data is not just
limited to flow rates of water or gas, but could include, for
example, typical total consumption per use of appliances, the time
of day of their usage and the duration of typical usage. Therefore,
even by measuring just the flow rate, discrimination can be made on
a probabilistic basis, between, say, running a shower in the middle
of the night (unlikely) compared with using a washing machine
programmed to operate overnight (more likely).
[0171] Following the analysis, in this example, the processor
produces a log of the electrical energy utilisation for each
appliance, comprising total energy consumption, time of day and
duration of each usage. This information is output by the output
section 40 to the user terminal 42 (such as a PC or a dedicated
device for utility-use feedback) so that the information can be
conveniently presented to the user.
[0172] Another embodiment of the invention will be described with
reference to FIG. 13 in which the same reference numerals indicate
the same parts as in FIGS. 1 and 12. In this case oil is supplied
from a supply 50, such as an oil storage tank, via a pump 52 to an
oil-burning heater 54, such as a domestic central heating boiler.
The utility meter 20 calculates when the pump 52 is operating, from
its electrical characteristics and so forth, in the same way as for
any other appliance 12 connected to the electricity supply 10. From
this operating information and known calibration characteristics of
the pump 52, the amount of oil delivered to the heater 54 can be
derived. In this way it is not necessary to provide a separate oil
meter, and the utility meter 20 can act as a combined utility
meter. Again this embodiment uses the general principle that values
representing the use of a first utility, in this case electricity,
are used to determine information on the usage of a second utility,
in this case oil.
[0173] In the embodiments of the invention described in this
section ("Multi-utility analysis"), pairs of utilities are
discussed, but the invention is not intended to be limited to only
two utilities. The utility meter could be concerned with more than
two utilities; for example measuring two utilities to derive
information about usage of a third utility, or measuring one
utility to infer information about the usage of two others, or in
general aggregating information about multiple utilities to improve
confidence in the inferred usage (for example by particular
appliances) of each one of the utilities.
[0174] The first stage in using the meter is the analysis stage as
already described in the three sections above to identify which
appliances are being used at any particular time and how much of
the or each particular utility they are consuming. The second stage
is to provide the user with short-term feedback via the user
terminal 42. For example, if the user terminal is a dedicated
device in a prominent place in the house, it could give immediate
feedback, for example that a particular appliance was left on
overnight when that is not usual. It could also highlight changes
in the behaviour of appliances, for example if an electric water
heater were running more frequently than usual, then the thermostat
might be faulty, or if the energy consumption by a refrigerator or
any other appliance showed an increase above an expected level,
then the user terminal could suggest that the appliance needs
servicing. Other examples of instant feedback, for utilities other
than electricity, might include warning the user that a tap has
been left running, or that a valve in a toilet cistern needs
replacing, or that a gas appliance has inadvertently been left
on.
[0175] A further use of the apparatus is to change the way billing
is done, by acting as a "smart meter". The data from the meter 20
can be transmitted automatically to a central unit via radio
frequency/mobile links which would eliminate the necessity for
manual reading of a meter and would also eliminate estimation of
meter readings. Billings and hence feedback can be carried out more
frequently which also has a positive impact on reducing the
quantity of energy being consumed.
[0176] A third stage in the use of the apparatus is long-term
feedback. For example, the user can perform trend analysis with the
user terminal 42, particularly if it is a personal computer. The
user can assess what behavioural changes have made the greatest
impact on reduced consumption; the user can compare his energy
usage profile with other users of similar sized properties, and
communities of users can engage in interactive activities, such as
exchanging tips on reducing usage and also in introducing a
competitive element to achieve the greatest reductions.
[0177] `Micro generation` is a growing phenomenon where homeowners
can install electricity generating equipment in their residence and
use it to provide some or all of the electricity generating needs.
The non-intrusive load monitoring (NILM) solution described herein
can complement a micro generation installation.
[0178] A micro-generation system supplies energy into the house and
thus can be metered by a NILM device since it will modify the
aggregate current waveform measured by the meter in an analogous
way to any other appliance, the difference being that the meter
will see a drop in power consumed by the house due to the
generation. Thus, one could look for a change in power in the house
and measure the change in `signature` and thus identify the source
of the change in power.
[0179] The primary benefit of this is that most micro generation
systems rely on a secondary electricity meter between the generator
and the rest of the electricity supply to measure the amount of
electricity generated (either for resale back to the grid, or for
home energy management). This secondary electricity meter would be
redundant and could be removed in the case that a NILM was
installed, since the NILM of the present invention could measure
the energy accurately without the need for a separate device.
[0180] Thus, according to a further embodiment of the invention,
one or more of the appliances 12 connected to the supply wiring 14
can be a generator of electrical power, for example a solar
photovoltaic panel or a wind turbine generator. As these devices
generate power, which is either fed to other appliances 12, or even
back to the supply utility 10, then the current and voltage
detected by the sensor 16 would also change, and the processor 26
can perform exactly the same analysis based on appliance data
stored in the store 28 to determine when each device is generating
power and the quantity generated. This gives convenient feedback
about the precise savings achieved by using the solar panel or wind
turbine, and also information about optimal siting of such
devices.
[0181] To further increase the accuracy, it is possible to fit a
low cost sensor (not shown) next to the micro-generation system
which would provide additional information to the NILM. For
example, in the case of a solar generation system, one could fit
one or more photodiodes next to the main solar array which would
feed back to the NILM. Thus changes in power could be correlated to
both the changes in power signature and changes in the observed
change in reading from the photodiode.
[0182] Advanced load management is also possible when the present
invention is used in conjunction with a micro-generation system.
The NILM has information pertaining to the energy consumption or
generation of every appliance in a distribution network. Hence, it
is able to coordinate energy usage in the home automatically. For
example, in the case that an electric car required recharging, it
could be plugged in to the wall at which point the NILM would
detect it and choose whether to switch it off, waiting for a point
when there is sufficient micro-generation capability available to
charge the car.
[0183] Although preferred embodiments of the invention have been
described, it is to be understood that these are by way of example
only and that various modifications may be contemplated.
* * * * *