基于聚类的HPO-BILSTM光伏功率短期预测

周育才; 肖添; 谢七月; 付强; 钟敏

doi:10.19912/j.0254-0096.tynxb.2023-0402

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (4) : 512-518. DOI: 10.19912/j.0254-0096.tynxb.2023-0402

基于聚类的HPO-BILSTM光伏功率短期预测

周育才¹, 肖添¹, 谢七月¹, 付强¹, 钟敏²

作者信息 +

CLUSTERING-BASED HPO-BILSTM SHORT-TERM PREDICTION OF PV POWER

Zhou Yucai¹, Xiao Tian¹, Xie Qiyue¹, Fu Qiang¹, Zhong Min²

Author information +

文章历史 +

摘要

考虑到光伏发电功率在不同天气类型下的波动性和不确定性,对此提出一种基于模糊C均值聚类算法（FCM）和猎食者优化算法（HPO）优化双向长短期记忆网络（BILSTM）的光伏发电短期功率预测模型。首先对光伏发电数据进行处理和分析,再进行主成分分析（PCA）降维和FCM聚类算法将数据按天气类型分为阴、晴、雨;最后通过HPO筛选得出BILSTM神经网络的最佳超参数,避免因超参数设置不佳对实验带来的影响,进一步提高实验的准确性和模型的泛化能力。最后通过预测和对比实验进行分析,验证所提方法的优越性。

Abstract

Considering the volatility and uncertainty of PV power generation under different weather types, a short-term power prediction model of PV power generation based on fuzzy C-mean clustering algorithm and predator optimization algorithm to optimize the bi-directional long short-term memory network is proposed. Firstly, the PV power generation data are processed and analyzed, then the principal component analysis downscaling and FCM clustering algorithm are performed to classify the data into cloudy, sunny and rainy according to weather types. Then, the best hyperparameters of the BILSTM neural network are derived through HPO screening, which avoids the impact of poor hyperparameter settings on the experiments and further improves the accuracy of the experiments and the generalization ability of the model. Finally, the superiority of the proposed method is verified by prediction and comparison experiments.

导出引用

周育才, 肖添, 谢七月, 付强, 钟敏. 基于聚类的HPO-BILSTM光伏功率短期预测[J]. 太阳能学报. 2024, 45(4): 512-518 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0402

Zhou Yucai, Xiao Tian, Xie Qiyue, Fu Qiang, Zhong Min. CLUSTERING-BASED HPO-BILSTM SHORT-TERM PREDICTION OF PV POWER[J]. Acta Energiae Solaris Sinica. 2024, 45(4): 512-518 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0402

中图分类号： TM615

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