Power generation forecasting for photovoltaic power stations using an ARMA-BP model

SHI Liang; SANG Min; JI Wenrui

doi:10.3969/j.issn.1672-3643.2025.03.001

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Power generation forecasting for photovoltaic power stations using an ARMA-BP model

更新时间：2025-09-24

- Power generation forecasting for photovoltaic power stations using an ARMA-BP model
- Ningxia Electric Power Issue 3, (2025)
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- DOI：10.3969/j.issn.1672-3643.2025.03.001
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- Published Online：27 June 2025，
  
  Published：2025
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SHI Liang, SANG Min, JI Wenrui. Power generation forecasting for photovoltaic power stations using an ARMA-BP model[J]. Ningxia electric power, 2025, (3).
DOI：

SHI Liang, SANG Min, JI Wenrui. Power generation forecasting for photovoltaic power stations using an ARMA-BP model[J]. Ningxia electric power, 2025, (3). DOI： 10.3969/j.issn.1672-3643.2025.03.001.

摘要

准确预测光伏电站发电量有利于电网公司安排常规发电与协调运行，为此，提出一种基于自回归移动平均值和反向传播(autoregressive moving average and back propagation

ARMA-BP)神经网络的光伏电站发电量预测方法。首先，分析发现光伏电站发电量数据具有线性和非线性趋势特征；其次，考虑 ARMA 模型较好的线性拟合能力和BP神经网络良好的非线性映射能力，提出了基于ARMA-BP的光伏电站发电量组合预测模型；最后，以某实际光伏电站为例验证了所提模型的有效性。与ARMA及BP神经网络的预测模型对比，所提组合预测模型具有更高的预测精度，预测效果更好，研究结果可为电网公司制定发电计划提供参考。

Abstract

Accurate forecasting of photovoltaic (PV) power generation is crucial for conventional power dispatch planning and system operation coordination.To this end

this paper proposes a forecasting method using a combined autoregressive 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.

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