
How to effectively detect abnormal electricity consumption behaviour from a large‐scale electrical load data is very important to smart grid. An abnormal electricity consumption analysis method based on density clustering is proposed. First, the similar users located in fix area are clustered in accordance with the electricity consumption characteristics. Then, on the basis of electricity consumption data sequence, the outlier electricity consumption for the similar users are obtained with density clustering. The matching degrees of these outliers can be calculated based on the similar user electricity consumption model and historical electricity consumption model. Finally, according to the threshold value, the abnormal electricity consumption can be discriminated with the comprehensive support degree. The simulation results show that this method can effectively identify the abnormal electricity consumption behaviour.
abnormal behaviour analysis algorithm, power engineering computing, similar user electricity consumption model, similar users, load forecasting, pattern clustering, outlier electricity consumption, power consumption, electricity consumption characteristics, historical electricity consumption model, large-scale electrical load data, Engineering (General). Civil engineering (General), electricity consumption data sequence, abnormal electricity consumption analysis method, density clustering, abnormal electricity consumption behaviour, TA1-2040
abnormal behaviour analysis algorithm, power engineering computing, similar user electricity consumption model, similar users, load forecasting, pattern clustering, outlier electricity consumption, power consumption, electricity consumption characteristics, historical electricity consumption model, large-scale electrical load data, Engineering (General). Civil engineering (General), electricity consumption data sequence, abnormal electricity consumption analysis method, density clustering, abnormal electricity consumption behaviour, TA1-2040
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