基于MFM-CG-DBN模型的高渗透率光伏微电网发电功率短期预测

徐小明; 王占海

doi:10.19912/j.0254-0096.tynxb.2024-1900

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (6) : 628-636. DOI: 10.19912/j.0254-0096.tynxb.2024-1900

基于MFM-CG-DBN模型的高渗透率光伏微电网发电功率短期预测

徐小明¹, 王占海²

作者信息 +

SHORT TERM PREDICTION OF HIGH PENETRATION PHOTOVOLTAIC MICROGRID POWER GENERATION BASED ON MFM-CG-DBN MODEL

Xu Xiaoming¹, Wang Zhanhai²

Author information +

文章历史 +

摘要

为提升高渗透率光伏微电网发电功率预测精准度,提出一种改进马尔科夫链位移时序预测法（MFM）,用以优化共轭梯度法（CG）-深度信念网络（DBN）组合预测模型（CG-DBN）存在的特征提取不充分、预测不精确问题。首先,借助Pearson相关系数分析高渗透率光伏微电网发电功率的影响因素;其次,借助马尔科夫链位移时序预测法具有的无后效性特点,将残差修正过程应用于CG-DBN预测模型,构建MFM-CG-DBN高渗透率光伏微电网发电功率短期预测模型;最后,利用MFM-CG-DBN短期预测模型对晴天、多云与雨天3类型天气下的高渗透率光伏微电网发电功率数据进行仿真。仿真结果显示,所提短期预测模型较传统CG-DBN短期预测模型具有更高预测精度,可满足高渗透率光伏微电网发电功率预测需求。

Abstract

To improve the accuracy of power generation prediction in high penetration photovoltaic microgrids, this paper proposes an improved Markov chain displacement time series prediction method （MFM） to optimize the problems of insufficient feature extraction and inaccurate prediction in the conjugate gradient method （CG） - deep belief network （DBN） combined prediction model （CG-DBN）. Firstly, using Pearson correlation coefficient to analyze the influencing factors of high penetration photovoltaic microgrid power generation; Secondly, taking advantage of the inefficiency of Markov chain displacement time series prediction method, the residual correction process is applied to the CG-DBN prediction model to construct a short-term prediction model for the power generation of MFM-CG-DBN high penetration photovoltaic microgrids; Finally, the MFM-CG-DBN short-term prediction model is used to simulate the power generation data of high penetration photovoltaic microgrids under three types of weather conditions： sunny, cloudy, and rainy. The simulation results show that the proposed short-term prediction model has higher prediction accuracy than the traditional CG-DBN short-term prediction model, and can meet the demand for high penetration photovoltaic microgrid power generation prediction.

导出引用

徐小明, 王占海. 基于MFM-CG-DBN模型的高渗透率光伏微电网发电功率短期预测[J]. 太阳能学报. 2026, 47(6): 628-636 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1900

Xu Xiaoming, Wang Zhanhai. SHORT TERM PREDICTION OF HIGH PENETRATION PHOTOVOLTAIC MICROGRID POWER GENERATION BASED ON MFM-CG-DBN MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(6): 628-636 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1900

中图分类号： TM73

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