With the aging of the social population structure, the number of empty-nesters is also increasing. Therefore, it is necessary to manage empty-nesters with data mining technology. This paper proposed an empty-nest power user identification and power consumption management method based on data mining. Firstly, an empty-nest user identification algorithm based on weighted random forest was proposed. Compared with similar algorithms, the results indicate that the performance of the algorithm is the best, and the identification accuracy of empty-nest users is 74.2%. Then a method for analyzing the electricity consumption behavior of empty-nest users based on fusion clustering index adaptive cosine K-means was proposed, which can adaptively select the optimal number of clusters. Compared with similar algorithms, the algorithm has the shortest running time, the smallest Sum of the Squared Error (SSE), and the largest mean distance between clusters (MDC), which are 3.4281 s, 31.6591 and 13.9513, respectively. Finally, an anomaly detection model with an Auto-regressive Integrated Moving Average (ARIMA) algorithm and an isolated forest algorithm was established. The case analysis shows that the recognition accuracy of abnormal electricity consumption for empty-nest users was 86%. The results indicate that the model can effectively detect the abnormal behavior of empty-nest power users and help the power department to better serve empty-nest users.
Keywords: abnormal power consumption detection; analysis of power consumption behavior; data mining; empty-nest user identification.