摘要
当前风功率预测的方法很多,但单一预测模型各有优劣且预测结果可靠性不高,难以满足电网对风功率预测精度的要求。针对该问题提出了一种基于反向传播神经网络与长短期记忆网络组合的预测模型用于多步风功率预测,并通过仿真验证了该组合模型在风功率预测中的可行性。仿真结果表明,该组合模型的均方根误差与绝对平均误差比反向传播神经网络与长短期记忆网络单一模型的误差结果都减小很多,表明该风功率组合模型在预测精度上比单一风功率预测模型更具有优越性,适用于实际的多步风功率预测。
There are many methods of wind power forecasting at present,but each forecasting model has its own advantages and disadvantages,and the reliability of the forecasting results is not high,so it is difficult to meet the requirements of the power grid for wind power forecasting accuracy.In order to deal with this problem,a forecasting model based on the combination of back propagation(BP)neural network and long and short teim memory(LSTM)network is proposed for multi-step wind power forecasting,and the feasibility of the combined model in wind power forecasting is verified by simulation.The simulation results show that,the root-mean—square error and absolute average error of the combined model are much smaller than those of the single model of BP neural network and LSTM network,indicating that the combined wind power model is more accurate than the single wind power forecasting model.Therefore,it is suitable for practical multi-step wind power forecasting.
作者
杜鸿飞
韩磊
贾峰生
DU Hongfei;HAN Lei;JIA Fengsheng(Shanxi Shijizhongshi Power Science&Technology Co.,Ltd.,Taiyuan,Shanxi 030006,China;School of Computer and Information Technology of Shanxi University,Taiyuan,Shanxi 030006,China)
出处
《山西电力》
2023年第2期17-20,共4页
Shanxi Electric Power
