Prediction of Individual Household Energy Bills Using Deep Learning

Energy bills are one of the most significant regular payments made by any household. The accurate estimation of the energy bills can help people make better decisions on their energy use. However, it is difficult for people to predict their monthly energy bills because of many influencing factors, such as weather and progressive rates. In this paper, we propose a deep learning-based prediction method of the energy bills for individual households, considering the weather. First, we proposed a gated recurrent unit-based model that can incorporate weather and date information and predict energy usage of electricity, water, and gas. Then, a weighted mean squared error is adopted for training of the proposed model for better accuracy. Finally, the predicted energy usage using the proposed model are converted to a monthly energy bill. The proposed approach is verified on the dataset consisting of energy usage data of 2,234 households and weather data of the Korea Meteorological Administration. The results show that our approach can accurately predict the energy bills of individual households with a small average error of 3,892 won.