Identifying Factors Affecting Household Energy Consumption Using Data Mining Methods

Due to increasing population and decreasing energy sources, this research studies the consumption of domestic energy. The purpose of this study is to predict the factors affecting household energy consumption. To do this, we use 3 algorithms, M5Rules, K-nearest neighbor and random forest, available in Weka software. In this study, the feature correlation algorithm is used to select the most important factors affecting energy consumption and their impact. The results show that lights and fixtures, temperature of the living room, outside temperature, temperature outside of Chievres Station, wind speed, humidity in the kitchen and the temperature in the laundry area have the most impact on household energy consumption. Among the methods, random forest algorithm presented the best results.