Short-term forecasting of electricity demand for the residential sector using weather and social variables

The aim of this study is to provide a precise model for the one-month-ahead forecast of electricity demand in the residential sector. In this study, a total of 20 influential variables are considered including monthly electricity consumption, 14 weather variables, and five social variables. Based on support vector regression and fuzzy-rough feature selection with particle swarm optimization algorithms, the proposed method established a model with variables that relate to the forecast by ignoring variables that inevitably lead to forecasting errors. The proposed forecasting model was validated using historical data from South Korea between January 1991 and December 2012. The first 240 months were used for training and the remaining 24 for testing. The performance was evaluated using MAPE, MAE, RMSE, MBE, and UPA values. Furthermore, it was compared with that obtained from the artificial neural network, auto-regressive integrated moving average, multiple linear regression models, and the methods proposed in the previous studies. It was found to be superior for every performance measure considered in this study. The proposed method has an advantage over the previous methods because it automatically determines appropriate and necessary variables for a reliable forecast. It is expected that it can contribute to more accurate forecasting of short-term electricity demand in the residential sector. The ability to accurately forecast short-term electricity demand can assist power system operators and market participants in ensuring sustainable electricity planning decisions and securing electricity supply for the consumers.