Mining Residential Power Consumption Behaviour Based on Association Rules

In recent years, with the clean energy consumption, decentralized supply and market-oriented trading, the composition of power system has become increasingly complex. As one of the most important consumer groups, the mining of residents' power consumption behavior is of great value to strengthen demand side management, improve energy efficiency and promote the development of smart grid. Therefore, this paper studies the pattern recognition and associated factors of power consumption behavior based on unsupervised clustering and Apriori. We use the hourly load curve of Shanghai residents from 2016 to 2018 to carry out the experiment. According to the results of the survey, we distinguish the single household characteristics and combined household characteristics, analyze their relationship with these typical power consumption modes, and eliminate the impact of unbalanced distribution of categories. The experimental results show that socio-economic factors, environmental cognitive factors and housing factors will affect Chinese residents' power consumption behavior to varying degrees. The association rules of combined household characteristics formed in different seasons are also quite different.