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Abnormal energy consumption detection using contextual grouping of smart meters
| Content Provider | Indraprastha Institute of Information Technology, Delhi |
|---|---|
| Author | Sial, Ankur |
| Abstract | Energy consumption is dependent on temperature, humidity, occupancy, occupant type, building area etc. All these factors collectively define the context of an energy meter. Once the context is known, the meters within the same context can be grouped and their behaviour can be analyzed together. This paper presents four heuristics, including one novel heuristic, to identify abnormal energy consumption. Using these heuristics, data collected from fifty smart meters deployed inside hostels of IIIT-Delhi was investigated for abnormal energy consumption detection. The anomalies and possible causes were discussed with IIIT-Delhi campus administrator. Energy consumption per occupant for one of the meters was found four times when compared to rest of the meters. This was detected using the heuristics mentioned in the paper. The results demonstrated that the proposed heuristics successfully found abnormal energy consumption behaviour. |
| File Format | |
| Language | English |
| Access Restriction | Authorized |
| Subject Keyword | Abnormal energy consumption Anomaly detection Smart meters K-means Principal component analysis Histogram buckets |
| Content Type | Text |
| Educational Degree | Bachelor of Technology (B.Tech.) |
| Subject | Data processing & computer science |
