Regression Analysis of Residential Electricity Consumption Behavior Based on Weighted Voting Ensemble Clustering

The analysis of residential electricity consumption behavior is the basis for exploring the potential of residential demand response and improving the level of electricity service. An ensemble clustering method based on weighted voting is proposed for residential customers' electricity daily load curve. Four commonly used clustering algorithms are treated as members for voting, and the clustering results of the member algorithms are ensembled by assigning weights according to the clustering validity index to combine the advantages of algorithms. The load curve characteristic index is extracted to obtain six typical electricity consumption patterns by weighted voting clustering of residential load curves, and multiple logistic regression is used to analyze the link between electricity consumption patterns and their household characteristics. The results of the case show that the proposed method improves the clustering effect with better robustness and show significant positive or negative correlations between electricity consumption patterns and several household characteristics.