Forecasting residential electricity consumption using a hybrid machine learning model with online search data

Accurate forecasting of residential electricity consumption plays an important role in formulating energy plans and ensuring the safety of power system operations. In order to improve forecasting accuracy, we propose a novel hybrid model with online search data for residential electricity consumption forecasting. Two main steps are involved: (1) Time difference correlation analysis, cointegration test, and Granger causality test are employed to investigate the relationship between online search data and residential electricity consumption. Qualified search keywords are selected to serve as predictors. (2) An extreme learning machine model optimized by Jaya algorithm, together with the selected search keywords from the first step, is proposed to predict residential electricity consumption. Furthermore, monthly residential electricity consumption data from China are used to validate the effectiveness of the proposed model. The experimental results show that the incorporation of online search data into the model can significantly improve forecasting accuracy. After incorporating online search data, improvement rates of all the forecasting models exceed 10%. In addition, the proposed model has the best forecasting performance compared with seasonal autoregressive integrated moving average (SARIMA(X)), support vector regression (SVR), back propagation neural network (BPNN) and extreme learning model (ELM). Root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) of the proposed model with online search data decrease by 34%-51.2%, 43.03%-53.92%, and 41.35%-54.85% relative to other benchmark models, respectively.