Study on Short-term Load Forecasting Based on Improved Random Forest Algorithm
Aiming at the low accuracy of short-term load forecasting in the power system,an improved Random Forest Regression Forecasting(RFRF)model based on the Sparrow Optimisation Algorithm is proposed.Firstly,the SSA optimization algorithm is used to iteratively optimize the relevant parameters such as the number of decision trees and node disaggregation of RFR to improve the forecasting performance of RFR,and the SSA-RFR regression forecasting model is obtained.In order to verify the model's excellence in prediction accuracy,data simulation is carried out using historical data of power load in a region of China,and the prediction results of the unimproved model are compared with those of the improved model,and the results show that the improved model proposed in this paper has a much better prediction accuracy,which is closer to the actual value.
power systemshort-term load forecastingrandom forestsparrow optimisation algorithmiterative optimisation
NETL
NSTL
万方数据
