宁夏电力Issue(3)：1-6,6.DOI:10.3969/j.issn.1672-3643.2025.03.001
基于ARMA-BP的光伏电站发电量预测
Power generation forecasting for photovoltaic power stations using an ARMA-BP model
石良 1桑敏 1冀文瑞1
作者信息
- 1. 国网山东省电力公司莱芜供电公司,山东 济南 271100
- 折叠
摘要
Abstract
Accurate forecasting of photovoltaic(PV)power generation is crucial for conventional power dispatch plan-ning and system operation coordination.To this end,this paper proposes a forecasting method using a combined autore-gressive moving average-back propagation(ARMA-BP)neural network model.First,an analysis reveals that PV generation data exhibit both linear and nonlinear characteristics.To address this,the ARMA model is employed to capture linear trends,while the BP neural network is used to model nonlinear patterns.A hybrid ARMA-BP model is thus constructed to leverage the strengths of both approaches.The effectiveness of the proposed model is verified through case studies at an actual PV power station.Compared with standalone ARMA and BP models,the combined ARMA-BP model achieves significantly improved prediction accuracy.The results offer a valuable refe-rence for grid operators in optimizing generation planning.关键词
光伏电站/发电量预测/组合预测模型/自回归移动平均值/反向传播神经网络Key words
photovoltaic power station/power generation forecasting/combined forecasting model/autoregressive moving average/back propagation neural network分类
动力与电气工程引用本文复制引用
石良,桑敏,冀文瑞..基于ARMA-BP的光伏电站发电量预测[J].宁夏电力,2025,(3):1-6,6.
