Electricity Customer Behavior Analysis Method Based on Adaptive Feature Weight Clustering Algorithm

Analysis of electricity customer behavior (ECB) is an important way for power companies to understand customer needs and preferences. The current complexity of ECB data is high, and traditional analysis methods are difficult to accurately and fully explore customer behavior characteristics. For the purpose of improving the accuracy of behavior analysis and enhance the service quality of power enterprises, this paper combines the adaptive feature weight clustering algorithm to conduct in-depth research on the behavior analysis method of electricity customers. This article first collected and processed customer behavior data from the databases of relevant business systems; Then it uses the K-means algorithm to cluster the data by considering feature weights; Finally, experimental analysis was conducted on it. The experimental findings demonstrate that compared to the traditional K-means algorithm, the average accuracy of the adaptive feature weight clustering algorithm in this paper is 14.1% higher. The conclusion indicates that the ECB analysis method based on adaptive feature weight clustering algorithm is helpful in mining and reflecting customer behavior characteristics, improving the service level and customer satisfaction of power enterprises.