基于Stacking-RBFNN的两段式月度电量连续预测  

Two-stage Continuous Monthly Electricity Forecasting Based on Stacking-RBFNN

作　　者：白雪 田传波 李建锋 张嘉埔 杜苁聪 武亚杰 BAI Xue;TIAN Chuanbo;LI Jianfeng;ZHANG Jiapu;DU Congcong;WU Yajie(China Electric Power Research Institute Co.,Ltd.,Haidian District,Beijing 100192,China)

机构地区：[1]中国电力科学研究院有限公司,北京市海淀区100192

出　　处：《电力信息与通信技术》2025年第3期1-8,共8页Electric Power Information and Communication Technology

基　　金：国家电网有限公司总部科技项目资助“面向新型电力系统的客户侧智能支撑服务关键技术研究”(SGZB0000YXJS2401055)。

摘　　要：当前电网负荷特性日趋复杂,为了有效支撑电力市场稳步推进,亟需开展电量连续预测研究。文章针对当前连续预测受未来数据缺失和误差传播,导致预测不准确的问题,提出了一种两步式的电量预测框架,充分利用了集成学习和径向基函数神经网络(radial basis function neural network,RBFNN)的优势。在第1阶段,对前半周期建立Stacking模型进行预测,在第2阶段,融合第1阶段预测结果,并使用RBFNN进行后半周期预测,最后通过中国西部某省的实际用电数据进行实验,证明了所提框架的有效性。At present,the load characteristics of the power grid are becoming more and more complex,and in order to effectively support the steady progress of the electricity market,it is urgent to carry out research on continuous forecasting of electricity.In order to solve the problem that the current continuous forecasting is inaccurate due to the lack of future data and the propagation of errors,a two-step electricity forecasting framework is proposed,which makes full use of the advantages of ensemble learning and radial basis function neural network(RBFNN).In the first stage,the stacking model is established for forecasting in the first half of the cycle,in the second stage,the forecasting results of the first stage are fused,and RBFNN is used to predict the second half of the cycle,and finally the effectiveness of the proposed framework is proved by experiments with the actual electricity consumption data of a province in western China.

关 键 词：连续电量预测 机器学习 神经网络 两阶段预测 

分 类 号：TM715[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]