Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition

• Published in: Energy (Oxford) 2018-12, Vol.165, p.1220-1227
• Main Authors: Li, Chuan, Tao, Ying, Ao, Wengang, Yang, Shuai, Bai, Yun
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Back propagation networks; Computer simulation; Consumption; Coupled modes; Decomposition; Electricity; Electricity consumption; Ensemble empirical mode decomposition; Errors; Fast Fourier transformation; Fast Fourier transformations; Feature extraction; Forecast; Forecasting; Fourier transforms; Marketing management; Neural networks; Random forest; Regression models; Support vector machines
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2018.10.113

The forecast of electricity consumption plays an essential role in marketing management. In this study, a random forest (RF) model coupled with ensemble empirical mode decomposition (EEMD) named EEMD-RF is presented for forecasting the daily electricity consumption of general enterprises. The candidate data is first decomposed into several intrinsic mode functions (IMFs) by the EEMD. Through fast Fourier transformation, the features in each IMF are extracted in the time-frequency domain, then simulated and predicted by the RF model. Finally, the results of each IMF are integrated into the overall trend of the daily electricity consumption for those enterprises. The proposed method was applied to two enterprises located in the Jiangsu High-Tech Zone, and the period of collected data was from January 1, 2015 to May 3, 2016. To show the applicability and superiority of the EEMD-RF approach, two basic models (a back-propagation neural network (BPNN) and least squares support vector regression (LSSVM) and five model experiments (EEMD-BPNN, EEMD-LSSVM, RF, BPNN and LSSVM) were selected for comparison. Among these approaches, the proposed model exhibited the best forecast performance in terms of mean absolute error, mean absolute percentage error, and root-mean-square error. •A random forest model coupled with ensemble empirical mode decomposition (EEMD-RF) is proposed.•The EEMD is applied for extracting complex features of different modes.•The RF is applied for modeling the changes of different modes.•The EEMD-RF has high accuracy in enterprise electricity consumption forecasting.