摘要
为提高母线负荷日前预测的精度和鲁棒性,提出一种基于最优相似日集与深度学习模型的综合母线负荷日前预测方法。首先,基于母线负荷和气象数据,采用灰色关联分析和熵权法对相似日进行评分,得到最优相似日集合;然后,训练多组极限学习机(extreme learning machine,ELM)模型参数,并采用一种变异果蝇优化算法(mutation fruit fly optimization algorithm,MFOA)优化ELM的权值和阈值,增强模型鲁棒性;最后,提出误差上限偏离度目标函数(deviation degree of prediction error,DPE)作为DPE-MFOA-ELM模型的目标函数,增强了母线负荷预测普适度。以10组不同类型母线负荷为例进行仿真测试,结果表明,相比于传统深度机器模型,所提方法提高了母线负荷的预测精度和鲁棒性。
To improve the accuracy and robustness of day-ahead bus load forecasting,this paper proposes a hybrid method integrating optimal similar-day sets and deep learning.First,using bus load and meteorological data,it scores similar day via gray relational analysis and entropy weight method to form the optimal similar-day set.Next,multiple sets of extreme learning machine(ELM)model parameters are trained,and the weights and thresholds of ELM are optimized using a mutation fruit fly optimization algorithm(MFOA)to enhance the robustness of the model..Finally,it introduces a deviation degree of prediction error(DPE)objective function for the DPE-MFOA-ELM model to strengthen the bus load forecasting applicability.Simulation tests on 10 diverse bus load groups show that,compared to conventional deep machine models,the proposed method improves the prediction accuracy and robustness of bus load.
作者
赵永波
林浩然
孔维娜
李开灿
窦震海
ZHAO Yongbo;LIN Haoran;KONG Weina;LI Kaican;Dou Zhenhai(State Grid Yutai Electric Power Company,Jining 272000,China;State Grid Jining Electric Power Company,Jining 272000,China;Shandong Guangheng Electric Technology Co.,Ltd.,Zibo 255400,China)
出处
《山东电力技术》
2025年第8期67-78,共12页
Shandong Electric Power
基金
国网山东省电力公司科技项目(520606230005)。
