A Two-Stage Feature Extraction Approach for Green Energy Consumers in Retail Electricity Markets Using Clustering and TF–IDF Algorithms

The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies, which poses greater challenges for the market service for green energy consumers. This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and term frequency-inverse document frequency (TF–IDF) algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market. First, the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete, interval, and relational features. A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts. Then, TF–IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters. Finally, the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China. It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages, whose averaged values are 25.64%, 50%, 39.66%, and 24.89% in four different clusters, respectively. Additionally, power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.