Fast detection of energy consumption anomalies in buildings

Patent metadata

Field	Value
Publication number	US-2019205774-A1
Application number	US-201815859963-A
Country	US
Kind code	A1
Filing date	Jan 2, 2018
Priority date	Jan 2, 2018
Publication date	Jul 4, 2019
Grant date	—

Abstract

Embodiments for detection of energy consumption anomalies in one or more energy consumption systems in a cloud computing environment by a processor. Energy consumption may be predicted for one or more facilities according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof. An onset of an energy consumption anomaly may be detected according to the prediction.

First claim

1 . A method for detection of energy consumption in a computing environment by a processor, comprising:
predicting energy consumption for one or more facilities according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof and detecting an onset of an energy consumption anomaly according to the prediction.

2 . The method of claim 1 , further including alerting one or more Internet of Things (IoT) devices of the energy consumption anomaly.

3 . The method of claim 1 , further including identifying a location of the energy consumption anomaly according to energy consumption data collected by one or more Internet of Things (IoT) sensor devices associated with the one or more facilities, wherein the one or more IoT sensor devices are in an IoT computing network.

4 . The method of claim 1 , further including monitoring the energy consumption using a change point detection operation, a hypothesis test, or a combination thereof.

5 . The method of claim 1 , further including cognitively learning or estimating one or more tuning parameters of one or more prediction models for predicting the energy consumption.

6 . The method of claim 1 , further including dynamically changing one or more tuning parameters of one or more prediction models for predicting the energy consumption.

7 . The method of claim 1 , further including comparing the predicted energy consumption with an energy consumption threshold to detect the energy consumption anomaly.

8 . A system for detection of energy consumption in a computing environment, comprising:
one or more computers with executable instructions that when executed cause the system to:
predict energy consumption for one or more facilities according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof; and
detect an onset of an energy consumption anomaly according to the prediction.

9 . The system of claim 8 , wherein the executable instructions further alert one or more Internet of Things (IoT) devices of the energy consumption anomaly.

10 . The system of claim 8 , wherein the executable instructions further identify a location of the energy consumption anomaly according to energy consumption data collected by one or more Internet of Things (IoT) sensor devices associated with the one or more facilities, wherein the one or more IoT sensor devices are in an IoT computing network.

11 . The system of claim 8 , wherein the executable instructions further monitor the energy consumption using a change point detection operation, a hypothesis test, or a combination thereof.

12 . The system of claim 8 , wherein the executable instructions further cognitively learn or estimate one or more tuning parameters of one or more prediction models for predicting the energy consumption.

13 . The system of claim 8 , wherein the executable instructions further dynamically change one or more tuning parameters of one or more prediction models for predicting the energy consumption.

14 . The system of claim 8 , wherein the executable instructions further compare the predicted energy consumption with an energy consumption threshold to detect the energy consumption anomaly.

15 . A computer program product for detection of energy consumption in a building associated with a computing environment by a processor, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:
an executable portion that predicts energy consumption for one or more facilities according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof; and an executable portion that detects an onset of an energy consumption anomaly according to the prediction.

16 . The computer program product of claim 15 , further including an executable portion that alerts one or more Internet of Things (IoT) devices of the energy consumption anomaly.

17 . The computer program product of claim 15 , further including an executable portion that identifies a location of the energy consumption anomaly according to energy consumption data collected by one or more Internet of Things (IoT) sensor devices associated with the one or more facilities, wherein the one or more IoT sensor devices are in an IoT computing network.

18 . The computer program product of claim 15 , further including an executable portion that monitors the energy consumption using a change point detection operation, a hypothesis test, or a combination thereof.

19 . The computer program product of claim 15 , further including an executable portion that:
cognitively learns or estimates one or more tuning parameters of one or more prediction models for predicting the energy consumption; and dynamically changes the one or more tuning parameters of the one or more prediction models for predicting the energy consumption.

20 . The computer program product of claim 15 , further including an executable portion that compares the predicted energy consumption with an energy consumption threshold to detect the energy consumption anomaly.

Assignees

• IBM

Inventors

• Ba Amadou
• Ploennigs Joern

Classifications

• G05B15/02

  electric · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• G06Q50/06

  Energy or water supply · CPC title