摘要
针对电力系统短期负荷预测精度低的特点,提出了一种基于麻雀优化算法的改进随机森林回归预测模型。利用SSA优化算法不断迭代优化RFR的决策树数量和节点分列等相关参数,提高RFR的预测性能,得到SSA-RFR回归预测模型。为验证模型在预测精度上的优良性,利用中国某地区电力负荷历史数据进行数据仿真,将未改进的模型与改进后的模型的预测结果进行对比。对比结果表明,提出的改进模型具有更优良的预测精度,与实际值更为接近。
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.
作者
陈逸飞
薛军伟
邱俊
Chen Yifei;Xue Junwei;Qiu Jun(School of Electrical and Control Engineering,Liaoning Technical University,Huludao Liaoning 125000,China)
出处
《现代工业经济和信息化》
2024年第4期218-220,225,共4页
Modern Industrial Economy and Informationization
关键词
电力系统
短期负荷预测
随机森林
麻雀优化算法
迭代优化
power system
short-term load forecasting
random forest
sparrow optimisation algorithm
iterative optimisation
作者简介
第一作者:陈逸飞(2001-),男,河南商丘人,辽宁工程技术大学本科在读,研究方向为电力系统及其自动化。
