Design of integrated clustering-association data mining model to study the electricity consumption behavior of industrial units

Due to the high share of electricity consumption in the country's industries, in recent years, various projects have been implemented, including reducing the amount of load by implementing nationwide blackouts during peak consumption times. Today, data mining is widely used as a process of discovering useful patterns from the database and one of the effective methods for analyzing, modeling and predicting energy consumption. In this study, an integrated clustering-association data mining model has been designed to investigate power consumption behavior to discover and extract the pattern from the power consumption data set of industrial units located in one of the industrial towns of Tehran province. Observations show that during the warm months of the year, the average consumption of high-consumption cluster units, which includes about 34% of the studied industrial units, is about 4.2 times the consumption of low-consumption clusters and about 1.7 times the consumption of medium clusters. By using the proposed model in this study, in addition to identifying high-consumption units and implementing smart and fair policies in forced shutdowns, it is possible to prevent damage caused by forced shutdowns and industrial units can be encouraged to optimize energy consumption. The innovative approach of this model is able to control large volumes of data for planning different areas with the aim of optimizing its energy consumption.