基于相似日理论和IPOA-ELM的短期光伏发电预测

孔令廉; 王海云; 黄晓芳

doi:doi:10.19912/j.0254-0096.tynxb.2024-0743

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基于相似日理论和IPOA-ELM的短期光伏发电预测

更新时间：2025-10-22

- 基于相似日理论和IPOA-ELM的短期光伏发电预测
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- 太阳能学报 2025年46卷第9期页码：463-473
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0743
  中图分类号： TK615
- 网络出版：2025-10-14，
  
  纸质出版：2025
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孔令廉, 王海云, 黄晓芳. 基于相似日理论和IPOA-ELM的短期光伏发电预测[J]. 太阳能学报, 2025,46(9):463-473.
孔令廉, 王海云, 黄晓芳. 基于相似日理论和IPOA-ELM的短期光伏发电预测[J]. 太阳能学报, 2025,46(9):463-473. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0743.

DOI：

摘要

针对规模化光伏发电系统在非理想天气条件下预测精度不足

进而引发电力系统调度计划实施困难的问题

提出基于相似日理论和改进鹈鹕算法（IPOA）优化极限学习机（ELM）的光伏功率预测方法。首先利用皮尔逊相关系数方法

筛选出与光伏发电相关的主要气象因素;然后结合欧氏距离与马氏距离的综合评价指标对各时间点的历史数据与待预测日之间的综合距离进行比较

以求得相似日;接着将相似日样本集输入构建好的IPOA-ELM功率预测模型进行训练

并基于实际测量数据

对比研究IPOA-ELM模型与POA-ELM、SCSO-ELM、GJO-ELM模型在预测精度方面的表现。经比较分析后得出：加权综合指标选取相似日能更加准确地反映每个时刻点之间的距离和分布特性;IPOA算法对比POA、SCSO和GJO

在收敛速度和适应度表现上均为最优;同时在不同天气条件下作光伏预测时

IPOA-ELM模型的预测均方根误差均低于其他对比模型。值得注意的是

在阴雨天气等出力波动较强的情况下

该模型仍展现出良好的稳定性。充分证明所应用的IPOA-ELM预测模型具有较强的适应能力和预测准确性。

Abstract

Aiming at the problem of insufficient prediction accuracy of large-scale photovoltaic power generation system under non-ideal weather conditions

which in turn triggers the difficulties in the implementation of the power system dispatch plan

a prediction method of photovoltaic power generation is proposed based on the similar day theory and the improved pelican optimization algorithm （IPOA） extreme learning machine （ELM）. First

the Pearson correlation coefficient method filters out meteorological factors that exhibit a significant correlation with PV power generation; then the combined Euclidean distance and Mahalanobis distance evaluation indexes are used to calculate the combined distance between the historical days and the days to be predicted at each time point to determine the similar days. Then

the sample set of similar days is input into the constructed IPOA-ELM power prediction model for training

and based on the actual measurement data

the performance of the IPOA-ELM model is compared with that of the POA-ELM

SCSO-ELM

and GJO-ELM models in terms of prediction accuracy. After comparative analysis

it is concluded that the selection of similar days for the weighted composite index can more accurately reflect the distance and distribution characteristics between each moment point; the IPOA algorithm is optimal in terms of convergence speed and adaptability compared with POA

SCSO and GJO; and the root-mean-square errors of the IPOA-ELM model are lower than the other comparative models in the prediction of photovoltaic power generation under different weather conditions. It is worth noting that the model still shows good stability under rainy weather. It is fully proved that the applied IPOA-ELM prediction model has strong adaptive ability and prediction accuracy.

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