Abstract
This paper presents two methods for detecting abnormal electricity consumption by utilizing usage patterns in the vicinity. The methods use contextual and factual information including, energy consumption patterns, nature of supply and category of day to logically group meters and find abnormalities. Using heuristics proposed in the paper, data collected from fifty smart meters deployed inside hostels of IIIT-Delhi were investigated for abnormal electricity consumption. Multiple abnormalities were found and their causes were verified after discussion with campus administrators. Our results show that the proposed heuristics successfully found abnormal energy consumption behavior. Therefore, these methods could be used for real-time abnormality detection. This will result in reducing operating costs by automatically detecting and reporting abnormalities without human intervention.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Sial, A., Singh, A., Mahanti, A., Gong, M. (2018). Heuristics-Based Detection of Abnormal Energy Consumption. In: Chong, P., Seet, BC., Chai, M., Rehman, S. (eds) Smart Grid and Innovative Frontiers in Telecommunications. SmartGIFT 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 245. Springer, Cham. https://doi.org/10.1007/978-3-319-94965-9_3
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DOI: https://doi.org/10.1007/978-3-319-94965-9_3
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