Ultrashort term photovoltaic power combinatorial forecasting model based on similar day clustering

CHANG Qingsong; YANG Zhao; YANG Yihun; LEI Yang; HE Xinlin

doi:10.19666/j.rlfd.202301023

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Ultrashort term photovoltaic power combinatorial forecasting model based on similar day clustering

Thermal Power Generation Vol. 52, Issue 11, Pages: 123-131(2023)

更新时间：2025-09-28

- Ultrashort term photovoltaic power combinatorial forecasting model based on similar day clustering
- 作者机构：
  
  1. 华能吉林发电有限公司九台电厂
  2. 西安热工研究院有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202301023
  CLC： TM615
- Published：2023
- 稿件说明：
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CHANG Qingsong, YANG Zhao, YANG Yihun, et al. Ultrashort term photovoltaic power combinatorial forecasting model based on similar day clustering[J]. Thermal Power Generation, 2023, 52(11): 123-131.
DOI：

CHANG Qingsong, YANG Zhao, YANG Yihun, et al. Ultrashort term photovoltaic power combinatorial forecasting model based on similar day clustering[J]. Thermal Power Generation, 2023, 52(11): 123-131. DOI： 10.19666/j.rlfd.202301023.

摘要

针对功率预测模型受光伏功率波动性影响导致预测精度低的问题，提出一种基于相似日聚类的光伏功率预测组合模型。首先，采取k-means聚类算法将原始功率数据按不同天气类型划分为晴天、雨天和多云3种相似日样本集，并利用变分模态分解（VMD）对相似日样本进行分解；其次，采用卷积神经网络优化支持向量机（CNN-SVM）和双向长短时记忆（Bi LSTM）神经网络2个单模型分别对分解后的功率数据进行预测叠加并将预测结果进行加权组合，利用网格搜索（GS）算法寻找最优组合权重，提升组合预测模型性能；最后，以澳大利亚某光伏电站1年实测数据为例，验证所提出光伏功率预测模型的有效性。实验结果表明：无论何种天气类型，所提出模型均能很好地对光伏功率实现预测，具有较强的适应性。

Abstract

Aiming at the problem of low prediction accuracy of single power prediction model due to the impact of photovoltaic power fluctuation

a combined photovoltaic power prediction model based on similar day clustering is proposed. Firstly

k-means clustering is selected to divide the original power data into three similar day sample sets of sunny

rainy and cloudy according to different weather types

and the variational mode decomposition(VMD) is used to decompose the similar day samples; Secondly

the convolution neural network is used to optimize the support vector machine(CNN-SVM) and bidirectional short-term and short-term memory(BiLSTM) neural network

respectively

to predict and superimpose the decomposed power data and combine the prediction results with weights

and the grid search algorithm(GS) is used to find the optimal combination weight to improve the performance of the combination prediction model. Finally

the validity of the PV power prediction model proposed in this paper is verified by the one-year measured data of a photovoltaic power station in Australia. The experimental results show that the model proposed in this paper can predict the photovoltaic power well and has strong adaptability no matter what weather type.

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