Identification Method of Electrical Load for Electrical Appliances Based on K-Means ++ and GCN

• Published in: IEEE access Vol. 9; pp. 27026 - 27037
• Main Authors: Gao, Mingze, Pan, Shibo, Chen, Sirui, Li, Yanan, Pan, Nan, Pan, Dilin, Shen, Xin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Algorithms; Artificial neural networks; Clustering; Clustering algorithms; Convolution; Convolutional neural networks; Curves; DBI coefficient; Electric appliances; Electrical loads; Electrical products; Electricity consumption; Entropy (statistics); Euclidean distance; FastDTW; graph convolutional neural network; Heuristic algorithms; Identification methods; K-Means ++ clustering; Measurement methods; Neural networks; Power load identification; Similarity; Sockets; Time series analysis; Weighting methods
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3057722

Most cluster identification studies regarding consumer electricity load is faced with problems of erroneous clustering method similarity, low clustering quality and poor identification accuracy. To solve these problems, this paper utilizes the elbow method, k- Means ++, entropy weight method and a graph convolutional neural network to provide a means for cluster identification based on electrical appliance power data collected via smart sockets. In this article, elbow and entropy weight methods were used to achieve the adaptive clustering algorithm. To obtain the electrical appliance load curves, Euclidean and dynamic time warping (DTW) distances were integrated and the similarity measurement method was used to improve the k-means ++ algorithm, which was then applied to data collected via smart socket clustering. Next, clustering results were input into the graph convolutional neural network (GCN) for identification purposes and appliance type information was obtained. Finally, experiments were conducted using electrical load data from 20 commercial users. The method used was a combination of the k-means algorithm and long short-term memory network (LSTM). The results show that under optimal K value conditions (as determined by the elbow score), the methods used in this paper have improved clustering quality and recognition accuracy, when compared to LSTM.