Feature extraction and clustering of electricity market data based on GCN and graph filtering

As the power grid undergoes transformation and the Internet’s influence grows, the electricity market is evolving towards informatization. Traditional power marketing methods struggle to cope with the vast and intricate data of the modern power market. To address this challenge, this study employs random forest and PBF models for processing electricity market data. Additionally, it introduces the spectral clustering algorithm for a comprehensive analysis. The experimental results successfully extract diverse electricity consumption characteristics. This approach aids in advancing the informatization of power grid enterprises, boosting their capacity to perceive power data, and furnishing decision-makers with dependable support.