Open Access


Load Forecasting of the Power System: An Investigation Based on the Method of Random Forest Regression

Fuyun Zhu, Guoqing Wu*
College of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215137, China
* Corresponding Author: Guoqing Wu. Email:
(This article belongs to this Special Issue: Advances in Modern Electric Power and Energy Systems)

Energy Engineering 2021, 118(6), 1703-1712.

Received 30 December 2020; Accepted 08 March 2021; Issue published 10 September 2021


Accurate power load forecasting plays an important role in the power dispatching and security of grid. In this paper, a mathematical model for power load forecasting based on the random forest regression (RFR) was established. The input parameters of RFR model were determined by means of the grid search algorithm. The prediction results for this model were compared with those for several other common machine learning methods. It was found that the coefficient of determination (R2) of test set based on the RFR model was the highest, reaching 0.514 while the corresponding mean absolute error (MAE) and the mean squared error (MSE) were the lowest. Besides, the impacts of the air conditioning system used in summer on the power load were discussed. The calculation results showed that the introduction of indexes in the field of Heating, Ventilation and Air Conditioning (HVAC) could improve the prediction accuracy of test set.


Mathematical model; machine learning; power load; HVAC

Cite This Article

Zhu, F., Wu, G. (2021). Load Forecasting of the Power System: An Investigation Based on the Method of Random Forest Regression. Energy Engineering, 118(6), 1703–1712.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1086


  • 611


  • 0


Share Link

WeChat scan