U.S. patent application number 15/279792 was filed with the patent office on 2018-03-29 for identifying and mitigating risk associated with weather conditions.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Melanie E. Roberts, Arun Vishwanath.
Application Number | 20180090929 15/279792 |
Document ID | / |
Family ID | 61686689 |
Filed Date | 2018-03-29 |
United States Patent
Application |
20180090929 |
Kind Code |
A1 |
Roberts; Melanie E. ; et
al. |
March 29, 2018 |
IDENTIFYING AND MITIGATING RISK ASSOCIATED WITH WEATHER
CONDITIONS
Abstract
A system to mitigate weather exposure risk includes a processor
operatively coupled to memory. The processor is configured to
generate predicted energy consumption based on a model for expected
energy usage in response to detection of a weather event, and
compare the predicted energy consumption data with energy
consumption data. An alert is generated based at least in part on
the comparison, and the alert is sent to one or more entities over
one or more networks via one or more delivery methods.
Inventors: |
Roberts; Melanie E.; (North
Melbourne, AU) ; Vishwanath; Arun; (Blackburn,
AU) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
61686689 |
Appl. No.: |
15/279792 |
Filed: |
September 29, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H02J 3/00 20130101; G08B
21/10 20130101; H02J 3/004 20200101; Y04S 10/50 20130101; G05B
15/02 20130101; H02J 2203/20 20200101; Y04S 40/20 20130101; G06N
7/005 20130101; Y02E 60/00 20130101; H02J 3/003 20200101 |
International
Class: |
H02J 3/00 20060101
H02J003/00; G06N 5/04 20060101 G06N005/04; G06N 99/00 20060101
G06N099/00; G08B 21/18 20060101 G08B021/18 |
Claims
1. A system comprising: at least one processor operatively coupled
to at least one memory; wherein the at least one processor is
configured to: in response to detection of a weather event,
generate predicted energy consumption data based on a model of
expected energy usage; compare the predicted energy consumption
data with energy consumption data, wherein the energy consumption
data comprises energy consumption data obtained via at least one
meter associated with the at least one network; generate at least
one alert based at least in part on the comparison; and send the at
least one alert to one or more entities over one or more networks
via one or more delivery methods; wherein the processor is further
configured to create the model for expected energy usage based on
one or more sets of data comprising a set of historical energy
usage data, a set of socio-economic data, a set of demographic
data, and a set of historical weather data, wherein the model for
expected energy usage is created as a function of one or more
variables.
2.-4. (canceled)
5. The system of claim 1, wherein the creation of the model
comprises a use of a statistical modeling process selected from the
group consisting of: machine learning and Gaussian.
6. The system of claim 1, wherein the comparison comprises a
calculation of a difference between the predicted energy
consumption data and the energy consumption data, and wherein the
at least one alert is generated in response to the calculated
difference exceeding a threshold.
7. The system of claim 6, wherein the threshold is determined based
on one or more pre-defined rules.
8. The system of claim 1, wherein the at least one delivery method
is determined in accordance with one or more pre-defined rules.
9. The system of claim 1, wherein the processor is further
configured to deliver at least one additional alert to the one or
more entities via the at least one delivery method in response to a
failure to receive an acknowledgment of at least one previous
alert.
10. A computer-implemented method comprising: in response to
detecting a weather event, generating predicted energy consumption
data based on a model for expected energy usage, wherein the model
for expected energy usage is created based on one or more sets of
data comprising a set of historical energy usage data, a set of
socio-economic data, a set of demographic data, and a set of
historical weather data, and further wherein the model for expected
energy usage is created as a function of one or more variables;
comparing the predicted energy consumption data with energy
consumption data, wherein the energy consumption data comprises
energy consumption data obtained via at least one meter associated
with the at least one network; generating at least one alert based
at least in part on the comparison; and sending the at least one
alert to one or more entities over one or more networks via one or
more delivery methods.
11.-13. (canceled)
14. The method of claim 10, wherein creating the model comprises
using a statistical modeling process selected from the group
consisting of: machine learning and Gaussian.
15. The method of claim 10, wherein the comparison comprises
calculating a difference between the predicted energy consumption
data and the energy consumption data, wherein the at least one
alert is generated in response to the calculated difference
exceeding a threshold.
16. The method of claim 15, wherein the threshold is determined
based on one or more pre-defined rules.
17. The method of claim 10, wherein the at least one delivery
method is determined in accordance with one or more pre-defined
rules.
18. The method of claim 10, further comprising delivering at least
one additional alert to the one or more entities via the at least
one delivery method.
19. The method of claim 18, wherein the at least one additional
alert is delivered to the one or more entities in response to
failing to receive an acknowledgment of at least one previous
alert.
20. An article of manufacture comprising a computer-readable
storage medium for storing computer-readable program code which,
when executed, causes a computer to: in response to detection of a
weather event, generate predicted energy consumption data based on
a model for expected energy usage, wherein the model for expected
energy usage is created based on one or more sets of data
comprising a set of historical energy usage data, a set of
socio-economic data, a set of demographic data, and a set of
historical weather data, and further wherein the model for expected
energy usage is created as a function of one or more variables;
compare the predicted energy consumption data with energy
consumption data, wherein the energy consumption data comprises
energy consumption data obtained via at least one meter associated
with the at least one network; generate an alert based at least in
part on the comparison; and send the at least one alert to one or
more entities over one or more networks via one or more delivery
methods.
21. The system of claim 1, wherein the set of historical energy
usage data is a set of historical electricity usage data.
22. The computer-implemented method of claim 10, wherein the set of
historical energy usage data is a set of historical electricity
usage data.
23. The article of manufacture of claim 20, wherein the set of
historical energy usage data is a set of historical electricity
usage data.
24. The article of manufacture of claim 20, wherein creating the
model comprises using a statistical modeling process selected from
the group consisting of: machine learning and Gaussian.
25. The article of manufacture of claim 20, wherein the comparison
comprises calculating a difference between the predicted energy
consumption data and the energy consumption data, wherein the at
least one alert is generated in response to the calculated
difference exceeding a threshold.
26. The article of manufacture of claim 20, further comprising
delivering at least one additional alert to the one or more
entities via the at least one delivery method.
Description
BACKGROUND
[0001] Temperature waves, such as heat waves and cold waves,
represent one of the leading natural disaster related causes of
death in many developed nations around the globe. In the United
States, for example, there are around 175 annual heat wave related
fatalities. The heat wave that struck Europe in 2003 has itself
been linked to over 70,000 fatalities. Despite the deadly effects
of heat waves, they are often absent from discussions regarding
emergency disasters and intervention. Heat waves disproportionately
impact vulnerable people, including the elderly, infirm, young, and
those lacking adequate shelter or temperature control systems.
Access to cooling systems (e.g., air conditioning systems) during a
heat wave or access to heating systems (e.g., central heating
systems) during a cold wave may be the difference between life and
death for those who are at-risk to these temperature waves.
However, vulnerable people may be unable to use these temperature
control systems due to incapacitation (e.g., fatigue, mobility
issues, etc.), due to financial constraints, or due to lack of
access to such temperature control systems in their homes.
SUMMARY
[0002] Illustrative embodiments of the invention provide techniques
for identifying and mitigating risk associated with a weather
condition. While illustrative embodiments are well-suited to
identifying and mitigating risk associated with an extreme weather
condition, alternative embodiments may be implemented.
[0003] For example, in one illustrative embodiment, a system to
mitigate risk associated with weather conditions comprises at least
one processor operatively coupled to memory. The at least one
processor is configured to generate predicted energy consumption
based on a model for expected energy usage in response to detection
of a weather event, and compare the predicted energy consumption
data with energy consumption data. An alert is generated based at
least in part on the comparison, and the alert is sent to one or
more entities over one or more networks via one or more delivery
methods.
[0004] For example, in another illustrative embodiment, a method
for mitigating risk associated with weather conditions comprises
generating predicted energy consumption based on a model for
expected energy usage in response to detecting a weather event, and
comparing the predicted energy consumption data with energy
consumption data. At least one alert is generated based at least in
part on the comparison, and the at least one alert is sent to one
or more entities over one or more networks via one or more delivery
methods.
[0005] For example, in another illustrative embodiment, an article
of manufacture to mitigate risk associated with weather conditions
comprises a computer-readable storage medium for storing
computer-readable program code which, when executed, causes a
computer to generate predicted energy consumption based on a model
for expected energy usage in response to detection of a weather
event, and compare the predicted energy consumption data with
energy consumption data. An alert is generated based at least in
part on the comparison, and the alert is sent to one or more
entities over one or more networks via one or more delivery
methods.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 depicts a block diagram illustrating an overview of a
system configured to identify and mitigate risk associated with an
extreme weather condition, according to an embodiment of the
invention.
[0007] FIG. 2 depicts a flow chart illustrating a process for
creating a model of expected energy usage, according to an
embodiment of the invention.
[0008] FIG. 3 depicts a flow chart illustrating a process for
identifying and mitigating risk associated with an extreme weather
condition, according to an embodiment of the invention.
[0009] FIG. 4 depicts a computer system in accordance with which
one or more components/steps of techniques of the invention may be
implemented, according to an embodiment of the invention.
[0010] FIG. 5 depicts a cloud computing environment, according to
an embodiment of the invention.
[0011] FIG. 6 depicts abstraction model layers, according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0012] In illustrative embodiments, techniques are provided for
identifying and mitigating risk associated with weather conditions.
More particularly, illustrative embodiments provide data analytics
techniques to predict electricity load to sustain a healthy
temperature during periods of extreme weather (e.g., during a heat
wave or a cold wave). As will be explained, the illustrative
embodiments advantageously leverage smart meter data to predict the
electricity load.
[0013] Conventional extreme weather warning systems identify
geographies, typically at the granularity of suburbs or local
government areas, which could be at risk. These warning systems
urge households to be prepared for evacuation when the announcement
is made by the relevant agencies. These recommendations are,
however, not personalized on the granularity of household
level.
[0014] With reference to FIG. 1, a block diagram is provided
illustrating a system for mitigating weather condition risk 100, in
accordance with an embodiment of the invention. System 100 may be
interpreted as being comprised of two "subsystems," including
subsystem 110 and subsystem 120. Although subsystems 110 and 120
are depicted in FIG. 1 as being individual subsystems of system
100, this depiction is purely exemplary for the ease in
description. For example, subsystems 110 and 120 may be
alternatively be embodied within a single combination, or as a
combination of sub-combinations of subsystems.
[0015] In one embodiment, subsystem 110 comprises components within
a building, such as a dwelling. For example, subsystem 110 may
comprise one or more Network of Things (NoT) components, or
primitives. In one embodiment, the NoT is an Internet of Things
(IoT). IoT is an instantiation of a NoT in which the components of
the NoT are tethered to the Internet. The one or more NoT
components may comprise one or more sensors, one or more
aggregators, one or more communication channels, one or more
external utilities (eUtilities) and at least one decision trigger.
Generally speaking, a sensor may be defined as an electronic
utility that measures one or more physical properties. An
aggregator may be defined as a software implementation based on one
or more mathematical functions that transform raw data into
aggregated data. A communication channel may be defined as a medium
by which data is transmitted (e.g., physical via Universal Serial
Bus (USB), wireless, wireless, wired, verbal, etc.). An eUtility
may be defined as a software or hardware product or service that
executes processes or feed data into the overall workflow of a NoT
(e.g., database, mobile device, software or hardware system, cloud,
computer, CPU, etc.). A decision trigger may be defined as a
conditional expression that triggers an action needed to satisfy
the purpose, specification and requirements of the NoT.
[0016] As shown in the illustrative embodiment of FIG. 1, subsystem
110 comprises components including smart meter 112, information
sensor(s) 114, smart door sensor(s) 116, at least one processor 113
and memory 115. In one embodiment, information sensor(s) 116 and
smart door sensor(s) 118 have Internet access capability. The
arrangement of the components of subsystem 110 depicted in FIG. 1
is not to be considered limiting.
[0017] In one embodiment, information sensor(s) 114 comprise one or
more smart location sensors configured to obtain identification
and/or location data. For example, an information sensor may be
configured to be wearable on an occupant of the building. A smart
location sensor is an electronic device that monitors the location
of the target subject in real-time or near real-time. The smart
location sensor may provide substantially precise information about
where an occupant is situated within the dwelling, as opposed to
just binary information indicating whether or not the dwelling is
occupied. For example, an elderly person who lives in the building
may choose to wear a smart location sensor in order to monitor a
location of the elderly person in real-time or in near-real time.
The information derived from the smart location sensor may be
communicated to one or more other devices locally or non-locally.
Examples of sensors that may be used to monitor the location of a
target subject include, but are not limited to, radio-frequency
identification (RFID) tags and location data obtained from a device
associated with the user, such as a smartphone, tablet, etc. (e.g.,
GPS coordinate data).
[0018] Smart door sensor(s) 116 is an electronic device that is
configured to record the opening and closing of doors. These doors
may be, for example, entrance doors to a building (e.g., dwelling),
internal doors within the building, or doors to appliances within
the building (e.g., a refrigerator door). The information
communicated by smart door sensor(s) 116 may be used locally to
control related systems. For example, the information communicated
by smart door sensor(s) 116 may include information pertaining to
the opening and closing of one or more doors in order to
dynamically adjust temperature control settings. Additionally, this
information may be communicated to linked systems and integrated
with additional information. For example, an alert may be triggered
if the front door of the building is opened during specified hours,
but the internal lights are not activated. Door activity data
associated with smart door sensor(s) 116 may be correlated with
additional data using a learning model or model to infer occupancy.
Accordingly, smart door sensor(s) 116 may be configured to support
real-time or near real-time occupancy monitoring.
[0019] Smart meter 112 is shown in communication with electrical
grid 130. A smart meter is an electronic device that records energy
consumption data and communicates the energy consumption data back
to a utility for monitoring and billing. For example, a smart meter
may be configured to record the energy consumption data in
scheduled intervals (e.g., every minute or every hour). The
temporal scale by which the smart meter is configured to record the
energy consumption data should be sufficiently fine for
implementation in accordance with the embodiments described herein.
A smart meter may include one or more real-time or near real-time
sensors, and may provide power outage notification, power quality
monitoring, and two-way communication with the utility.
Accordingly, a smart meter may be configured to support real-time
or near real-time energy monitoring functionality.
[0020] Energy consumption data obtained from meter 112 may be
correlated with additional data using a "learning" model, or model.
The additional data may comprise a comfortable indoor temperature
associated with one or more specific individuals or occupants of
the dwelling. The additional data may further comprise data related
to occupant demographic, age, health status, etc. In one
embodiment, the model may be used to predict energy consumption for
the dwelling. A statistical modeling process, such as a machine
learning algorithm, Gaussian process, etc. may be used to train the
model to offer better energy consumption predictions over time. For
example, the model may be updated in response to changes in energy
consumption, changes in occupants (e.g., demographics, ages, and
health status), etc.
[0021] In one embodiment, system 100 is configured to determine the
comfortable indoor temperature as a function of one or more
variables. For example, the one or more variables may comprise one
or more of ambient temperature, occupant demographic(s), age,
health status, or any other related variable. In one embodiment,
the one or more variables are weighted. For example, a variable may
be multiplied by a respective weight value in order to prioritize
certain variables in the determination of the comfortable indoor
temperature.
[0022] System 100 may be configured to support a single occupant
scenario, or a multiple occupant scenario. In other words, the
comfortable indoor temperature may be computed for a single
occupant, or may be computed for occupants associated with
different demographics. In the single occupant scenario, the
comfortable indoor temperature may be calculated by a function
comprising the following inputs: {A.times.ambient temperature,
B.times.demographic, C.times.age, D.times.health status, . . . },
where A, B, C and D are weights assigned to the ambient
temperature, demographic, age and health status, respectively. In
the multiple occupant scenario, each variable associated with a
respective occupant may be assigned its own weight. For example,
the demographic, age, health status, etc. of each occupant may be
assigned its own weight. In other words, in the multiple occupant
scenario, the comfortable indoor temperature may be calculated as a
function comprising the following inputs: {A.times.ambient
temperature, B.sub.i.times.demographic.sub.i,
C.sub.i.times.age.sub.i, D.sub.i.times.health status.sub.i, . . .
}, where A represents the weight assigned to the ambient
temperature, and B.sub.i, C.sub.i, and D.sub.i are weights assigned
to the demographic, age and health status corresponding to the i-th
occupant, respectively. An exemplary a function that may be used to
calculate the appropriate indoor temperature is:
15 ( 1 + h 6 ) + 2 g + ( 5 + h ) sin ( .pi. ( t - 2 ) 24 ) .times.
If a > 40 , then ( 80 - a 80 ) + 1 2 , else 1 + If a < 40 ,
then ( 1 a - 40 ) , else 1 , ##EQU00001##
where a is the age (in years) of the occupant, g represents gender
and can be 1 if the occupant is of a first gender type and 0 if not
of the first gender type, h.epsilon.[0,1] representing a health
status (e.g., 1 may represent a person in good health), and t is
the hour of the day. Further details regarding the creation of the
models are described herein with reference to FIG. 2.
[0023] In one embodiment, subsystem 120 comprises a computer system
or server. For example, as shown, subsystem 120 may comprise
processor 122, memory 124, and alert generation module 126. Alert
generation module 126 operates to generate an alert for delivery to
one or more entities during an extreme weather event (e.g., an
extremely hot day) based on data obtained from subsystem 110. In
one embodiment, energy consumption data obtained by meter 112 may
be compared to predicted energy consumption data obtained via the
model, and an alert may be generated based on the comparison. For
example, the alert may be generated in response to a difference in
the energy consumption and the predicted energy consumption
exceeding a threshold. In an alternative embodiment, subsystem 110
is configured to perform an additional step to determine if the
dwelling is occupied, and alert generation module 126 is configured
to generate an alert if both the difference in the energy
consumption and the predicted energy consumption exceeds a
threshold, and the dwelling is occupied.
[0024] In one embodiment, smart meter 112, information sensor(s)
114 and smart door sensor(s) 116 send their data directly to
subsystem 120. In an alternative embodiment, subsystem 110 is
configured to aggregate or compile data from smart meter 112,
information sensor(s) 114 and smart door sensor(s) 116, and send
the aggregated data to subsystem 120.
[0025] The generated alert serves as information relating to an
at-risk occupant of the dwelling. The one or more entities that may
receive the generated alert may include appropriate parties, such
as the occupant, a family member or other relative, a friend, a
landlord, or any other party that may want to be notified. The one
or more entities may further include one or more emergency services
that may be dispatched to rescue an at-risk person during an
extreme weather event. In one embodiment, the alert is sent to the
one or more entities via at least one delivery method. For example,
the alert may be sent to one or more entities via one or more of
application, or app alert (e.g., push notification), text, e-mail,
phone call with a pre-defined message, etc. An app alert is an
alert associated with an application installed on a device, such as
a smartphone or tablet. The app alert may include, for example, one
or more of a visual alert, vibrational alert and a sound alert.
Further details regarding the process of alert generation are
described herein with reference to FIG. 3.
[0026] In one embodiment, subsystem 120 is configured to
incorporate social context data for generating and/or sending
alerts. The social context data may be derived from various sources
that include, but are not limited to, credit card transactions,
health records, social media data, IoT sensor data (e.g., from
connected devices), phone transactions, e-mail data, wearable
fitness or health devices, and implantable health devices (e.g., an
insulin pump).
[0027] In one embodiment, subsystem 120 is configured to
incorporate the social context data by utilizing the social context
data as an input to the model for expected energy usage and safe
temperatures. For example, if credit card transactions indicate
that the occupant has made purchases at the local pharmacy on two
out of the past three days, a health score associated with the
occupant may be adjusted downward. Such an adjustment may
correspond to a change in a safe temperature range and hence the
expected energy usage.
[0028] In on embodiment, the social context data may be used to
determine the alert and the manner of the alert. For example, the
social network data may indicate that a first user is
well-connected with a second user. If the first user is observed to
be using his or her expected energy and the second user is not
observed to be using his or her expected energy, an alert may be
sent to the first user indicating that the second user is not using
his or her expected energy. That is, during an extremely hot day or
an extremely cold day, an alert may be sent to the first user
indicating that the second user may be residing in a dangerous
dwelling. The alert may further include a suggestion to invite the
second user over to share in the first user's
temperature-controlled home.
[0029] The system of FIG. 1 is configured to mitigate weather
condition risk, in real-time or near real-time, by generating an
alert in response to an extreme weather event. With reference to
FIG. 2, flow diagram 200 is provided illustrating a process for
mitigating weather condition risk. At step 210, one or more sets of
data are obtained. In one embodiment, the one or more sets of data
may include a set of historical electricity usage data, a set of
socio-economic data, a set of demographic data and a set of
historical weather data. It is to be understood and appreciated
that set of historical electricity usage data, set of
socio-economic data, set of demographic data and set of historical
weather data are not to be considered limiting.
[0030] The set of historical electricity usage data may be obtained
from a smart meter. For example, the set of historical electricity
usage data may comprise a historical electricity usage data
obtained from a smart meter for an individual. Additionally, the
set of historical electricity usage data may further comprise
historical electricity usage data obtained from a smart meter for
like individuals. The set of demographic data may include data
relating to the demographic of individuals living in a dwelling.
For example, the set of demographic data may indicate a single
demographic dwelling, or may indicate a multiple demographic
dwelling.
[0031] At step 220, a model of expected energy usage is created
based on the one or more sets of data. In one embodiment, the model
of expected energy usage is a function of one or more variables.
For example, the model of expected energy usage may be a function
of ambient temperature, humidity, etc. In one embodiment, the model
of expected electricity usage is specific to a dwelling and its
occupants. For example, the model of expected electricity usage may
be specific to a single demographic dwelling, or a multiple
demographic dwelling. The model of expected electricity usage may
adjusted to account for typical use, which may be affected by the
energy efficiency of the lifestyle of the occupants, electrical
equipment of the dwelling, etc. In one embodiment, creating the
model of expected electricity usage comprises using a statistical
modeling process. For example, the model may be created via a
machine learning process, a Gaussian process, etc.
[0032] The model of expected energy usage may be used to send a
warning that a person may be at risk during an extreme weather
event, such as extreme heat or extreme cold. With reference to FIG.
3, a flow chart 300 is provided illustrating a process for
mitigating risk during a weather event, such as an extreme weather
event. At step 310, a weather event is detected. At step 320
predicted energy consumption data is generated based on the model
of expected energy usage, and at step 330, energy consumption data
(e.g., electricity usage data) is received. In one embodiment, the
energy consumption data comprises real-time stream of electricity
usage data. The real-time stream of energy consumption data may be
obtained via one or more smart meters associated with the
dwelling.
[0033] At step 340, a comparison between the predicted energy
consumption data and the energy consumption data is performed by
calculating a difference between the predicted energy consumption
data and the energy consumption data.
[0034] At step 350, it is determined if the difference exceeds a
threshold. The threshold may be determined based on alert criteria.
The alert criteria may indicate, for example, that an alert is
generated when the difference between the load associated with the
electricity usage data and the load estimated by the model of
expected energy usage exceeds 5%. In one embodiment, the alert
criteria is a function of the demographics of the dwelling. The
alert criteria may be set manually by the end user, or by using
pre-defined rules.
[0035] If the difference does not exceed the threshold, the process
reverts back to step 330. In one embodiment, if the difference does
exceed the threshold, it is determined at step 360 if the dwelling
is currently occupied. For example, the resolution of smart meter
data may not be as fine-grained as that of a real-time or near
real-time location sensor to automatically determine if the
dwelling is occupied or not. If the dwelling is determined to be
unoccupied, the process reverts back to step 320. However, if the
dwelling is determined to be occupied, the process goes to step 370
to generate and send an alert to one or more entities. The one or
more entities may include appropriate parties, such as a family
member or other relative, a friend, a landlord, or any other party
that may want to be notified. In one embodiment, the alert is sent
to the one or more entities via at least one delivery method. For
example, the alert may be delivered to one or more entities via one
or more of app alert (e.g., push notification), text, e-mail, phone
call with a pre-defined message, etc. In one embodiment, the alert
may be sent to the one or more entities in accordance with one or
more pre-defined rules. The pre-defined rules may determine the one
or more entities, the at least one delivery method, etc.
[0036] In one embodiment, at step 380, it is determined if an
acknowledgement of a previous alert is received. If the
acknowledgement is received, the process ends since the warning has
been successfully broadcast to the one or more entities. However,
if the acknowledgment is not received, a secondary or additional
alert may be generated and sent to the one or more entities at step
390. In one embodiment, the additional alert may be sent to the one
or more entities via the at least one delivery method. The
additional alert may be sent to the one or more entities in
accordance with one or more pre-defined rules. The pre-defined
rules may comprise the same pre-defined rules discussed above in
step 370, or may comprise different pre-defined rules from the
pre-defined rules discussed above in step 370.
[0037] One or more embodiments can make use of software running on
a computer or workstation. With reference to FIG. 4, in a computing
node 410 there is a system/server 412, which is operational with
numerous other general purpose or special purpose computing system
environments or configurations. Examples of well-known computing
systems, environments, and/or configurations that may be suitable
for use with system/server 412 include, but are not limited to,
personal computer systems, server computer systems, thin clients,
thick clients, handheld or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
[0038] System/server 412 may be described in the general context of
computer system executable instructions, such as program modules,
being executed by a computer system. Generally, program modules may
include routines, programs, objects, components, logic, data
structures, and so on that perform particular tasks or implement
particular abstract data types. System/server 412 may be practiced
in distributed cloud computing environments where tasks are
performed by remote processing devices that are linked through a
communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
[0039] As shown in FIG. 4, system/server 412 is shown in the form
of a computing device. The components of system/server 412 may
include, but are not limited to, one or more processors or
processing units 416, system memory 428, and bus 418 that couples
various system components including system memory 428 to processor
416.
[0040] Bus 418 represents one or more of any of several types of
bus structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
[0041] System/server 412 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by system/server 412, and it includes both volatile
and non-volatile media, removable and non-removable media.
[0042] The system memory 428 can include computer system readable
media in the form of volatile memory, such as random access memory
(RAM) 430 and/or cache memory 432.
[0043] System/server 412 may further include other
removable/non-removable, volatile/nonvolatile computer system
storage media. By way of example only, storage system 334 can be
provided for reading from and writing to a non-removable,
non-volatile magnetic media (not shown and typically called a "hard
drive"). Although not shown, a magnetic disk drive for reading from
and writing to a removable, non-volatile magnetic disk (e.g., a
"floppy disk"), and an optical disk drive for reading from or
writing to a removable, non-volatile optical disk such as a CD-ROM,
DVD-ROM or other optical media can be provided. In such instances,
each can be connected to bus 318 by one or more data media
interfaces.
[0044] As depicted and described herein, memory 428 may include at
least one program product having a set (e.g., at least one) of
program modules that are configured to carry out the functions of
embodiments of the invention. A program/utility 440, having a set
(at least one) of program modules 442, may be stored in memory 428
by way of example, and not limitation, as well as an operating
system, one or more application programs, other program modules,
and program data. Each of the operating system, one or more
application programs, other program modules, and program data or
some combination thereof, may include an implementation of a
networking environment. Program modules 442 generally carry out the
functions and/or methodologies of embodiments of the invention as
described herein.
[0045] System/server 412 may also communicate with one or more
external devices 414 such as a keyboard, a pointing device, an
external data storage device (e.g., a USB drive), display 424, one
or more devices that enable a user to interact with system/server
412, and/or any devices (e.g., network card, modem, etc.) that
enable system/server 412 to communicate with one or more other
computing devices. Such communication can occur via I/O interfaces
422. Still yet, system/server 412 can communicate with one or more
networks such as a LAN, a general WAN, and/or a public network
(e.g., the Internet) via network adapter 420. As depicted, network
adapter 420 communicates with the other components of system/server
412 via bus 418. It should be understood that although not shown,
other hardware and/or software components could be used in
conjunction with system/server 412. Examples include, but are not
limited to, microcode, device drivers, redundant processing units,
external disk drive arrays, RAID systems, tape drives, and data
archival storage systems, etc.
[0046] It is to be understood that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0047] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0048] Characteristics are as follows:
[0049] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0050] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0051] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0052] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0053] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported, providing
transparency for both the provider and consumer of the utilized
service.
[0054] Service Models are as follows:
[0055] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0056] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0057] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0058] Deployment Models are as follows:
[0059] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0060] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0061] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0062] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0063] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
[0064] Referring now to FIG. 5, illustrative cloud computing
environment 550 is depicted. As shown, cloud computing environment
550 includes one or more cloud computing nodes 510 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 554A,
desktop computer 554B, laptop computer 554C, and/or automobile
computer system 554N may communicate. Nodes 510 may communicate
with one another. They may be grouped (not shown) physically or
virtually, in one or more networks, such as Private, Community,
Public, or Hybrid clouds as described hereinabove, or a combination
thereof. This allows cloud computing environment 550 to offer
infrastructure, platforms and/or software as services for which a
cloud consumer does not need to maintain resources on a local
computing device. It is understood that the types of computing
devices 554A-N shown in FIG. 5 are intended to be illustrative only
and that computing nodes 510 and cloud computing environment 550
can communicate with any type of computerized device over any type
of network and/or network addressable connection (e.g., using a web
browser).
[0065] Referring now to FIG. 6, a set of functional abstraction
layers provided by cloud computing environment 550 (FIG. 5) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 6 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0066] Hardware and software layer 660 includes hardware and
software components. Examples of hardware components include:
mainframes 661; RISC (Reduced Instruction Set Computer)
architecture based servers 662; servers 663; blade servers 664;
storage devices 665; and networks and networking components 666. In
some embodiments, software components include network application
server software 667 and database software 668.
[0067] Virtualization layer 670 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 671; virtual storage 672; virtual networks 673,
including virtual private networks; virtual applications and
operating systems 674; and virtual clients 675.
[0068] In one example, management layer 680 may provide the
functions described below. Resource provisioning 681 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 682 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may include application software licenses.
Security provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 683 provides access to the cloud computing environment for
consumers and system administrators. Service level management 684
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 685 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0069] Workloads layer 690 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 691; software development and
lifecycle management 692; virtual classroom education delivery 693;
data analytics processing 694; transaction processing 695; and
extreme weather monitoring 696, which may perform various functions
described above.
[0070] The embodiments described herein advantageously provide for
alert generation during an extreme weather event, such as extreme
heat, extreme cold, etc. . . . . For example, the embodiments
described herein advantageously leverage data obtained from a smart
meter, among other data, to determine whether or not a person may
be at-risk during the extreme weather event. The alert may be
generated in real-time, or near real-time, based on a comparison of
energy consumption with predicted energy consumption.
Advantageously, the predicted energy consumption may be obtained
from a model of expected energy consumption. The model of expected
energy consumption may be customized based on data corresponding to
one or occupants of a dwelling. Taking such personalized occupant
data into consideration advantageously provides for autonomous
alert generation tailored to a specific dwelling. The embodiments
described herein can allow for an adjustment of the model based on
changes in energy consumption and/or characteristics of the
dwelling over time, thereby refining the alert generation
process.
[0071] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0072] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0073] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0074] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
[0075] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0076] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0077] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0078] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0079] Although illustrative embodiments have been described herein
with reference to the accompanying drawings, it is to be understood
that the invention is not limited to those precise embodiments, and
that various other changes and modifications may be made by one
skilled in the art without departing from the scope or spirit of
the invention.
* * * * *