DETECTING ELECTRICITY THEFT VIA METER TAMPERING USING STATISTICAL METHODS
First Claim
1. A computer-implemented method for detecting anomalous energy usage amongst building and household entities, said method comprising:
- receiving, at a computing system, data comprising energy usage data relating to a building'"'"'s actual energy use over a defined time period, characteristics data of the building, and weather data over one or more defined time periods;
clustering buildings in one or more clusters as determined based on a building'"'"'s energy usage in each time period;
identifying buildings having energy usage that migrate from one cluster to another cluster between time periods,generating a model to predict a building'"'"'s energy usage, said model defining expected bounds of energy consumption given time of day (shift) and weather and building characteristics data received;
comparing energy usage for each building against an energy use predicted by the model for said building; and
identifying, from said comparison, buildings whose electricity usage is not predicted by model;
wherein said buildings identified as migrating from one cluster to another cluster between time periods, and said buildings exhibiting electricity usage not predicted by said generated model are flagged as anomalous energy usage entities.wherein a processing unit of said computer system is configured to perform said receiving, clustering, identifying of migrating buildings, model generating, comparing, and said identifying of buildings from said comparison.
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Abstract
A method for detecting anomalous energy usage of building or household entities. The method applies a number of successively stringent anomaly detection techniques to isolate households that are highly suspect for having engaged in electricity theft via meter tampering. The system utilizes historical time series data of electricity usage, weather, and household characteristics (e.g., size, age, value) and provides a list of households that are worthy of a formal theft investigation. Generally, raw utility usage data, weather history data, and household characteristics are cleansed, and loaded into an analytics data mart. The data mart feeds four classes of anomaly detection algorithms developed, with each analytic producing a set of households suspected of having engaged in electricity theft. The system allows a user to select households from each list or a set based on the intersection of all individual sets.
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Citations
13 Claims
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1. A computer-implemented method for detecting anomalous energy usage amongst building and household entities, said method comprising:
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receiving, at a computing system, data comprising energy usage data relating to a building'"'"'s actual energy use over a defined time period, characteristics data of the building, and weather data over one or more defined time periods; clustering buildings in one or more clusters as determined based on a building'"'"'s energy usage in each time period; identifying buildings having energy usage that migrate from one cluster to another cluster between time periods, generating a model to predict a building'"'"'s energy usage, said model defining expected bounds of energy consumption given time of day (shift) and weather and building characteristics data received; comparing energy usage for each building against an energy use predicted by the model for said building; and identifying, from said comparison, buildings whose electricity usage is not predicted by model; wherein said buildings identified as migrating from one cluster to another cluster between time periods, and said buildings exhibiting electricity usage not predicted by said generated model are flagged as anomalous energy usage entities. wherein a processing unit of said computer system is configured to perform said receiving, clustering, identifying of migrating buildings, model generating, comparing, and said identifying of buildings from said comparison. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented method for detecting anomalous energy usage amongst building and household entities, said method comprising:
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receiving, at a computing system, data comprising energy usage data relating to a building'"'"'s actual energy use over a defined time period, characteristics data of the building, and weather data over one or more defined time periods; running a first anomaly energy use detection scheme to cluster buildings in one or more clusters as determined based on a building'"'"'s energy usage in each time period and identify buildings having energy usage that migrate from one cluster to another cluster between time periods; running a second anomaly energy use detection scheme to generate a non-linear regression model to predict a building'"'"'s energy usage, said model defining expected bounds of energy consumption given time of day (shift) and weather and building characteristics data received, and identifying buildings whose electricity usage is not predicted by model; and running a third anomaly energy use detection scheme to obtain from said generated non-linear regression model, a pattern of residuals of the generated model over time, a residual representing a difference between the energy actual and predicted energy use for a building, and identifying buildings demonstrating non-randomness of its obtained pattern of residuals; and running a fourth anomaly energy use detection scheme to estimate a sigma and theft likelihood field {Ln,t} of each building and each day, and obtaining a theft likelihood field for all the shifts; and
, invoke a rule applied to said theft likelihood field; andflagging anomalous energy usage buildings as buildings identified as;
having energy usage that migrate from one cluster to another cluster between time periods;
having electricity usage not predicted by said model;
exhibiting demonstrating non-randomness of its obtained pattern of residuals;
or have a residual energy computed for a building as exceeding a certain distance from an expected residual mean based on said rule;
or combinations thereof. - View Dependent Claims (12, 13)
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Specification