基于多元气象信息和改进组合神经网络的分布式光伏短期功率预测模型

吴伟丽; 米婵; 李磊

doi:10.19912/j.0254-0096.tynxb.2024-1159

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (11) : 181-192. DOI: 10.19912/j.0254-0096.tynxb.2024-1159

基于多元气象信息和改进组合神经网络的分布式光伏短期功率预测模型

吴伟丽^1,2, 米婵^1,2, 李磊^1,2

作者信息 +

DISTRIBUTED PHOTOVOLTAIC SHORT-TERM POWER PREDICTION MODEL BASED ON MULTIVARIATE METEOROLOGICAL INFORMATION AND IMPROVED COMBINED NEURAL NETWORK

Wu Weili^1,2, Mi Chan^1,2, Li Lei^1,2

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文章历史 +

摘要

为提高光伏发电功率预测的准确性,提出一种考虑邻近电站气象信息的多元气象特征和改进组合神经网络的光伏功率短期预测模型。首先,考虑相邻分布光伏电站之间的地理因素和气候条件的相关性,利用灰色关联法确定待预测电站的主要影响因素,构成多元气象信息关键特征作为预测模型的输入序列。其次,结合时间卷积网络（TCN）对输入序列信息有效提取和双向门控循环单元（BiGRU）对数据双向学习的优势,搭建TCN-BiGRU组合预测模型,并采用改进后的灰狼优化算法（IGWO）对BiGRU进行超参数寻优,实现光伏发电功率的高精度预测。最后,利用实测数据对所提模型加以验证,并与同类方法进行对比。结果表明与多元气象信息结合,预测模型能够有效提高一年四季中不同类型天气的发电功率预测精度;与其他预测模型相比较,即使在气候条件剧烈变化或随机变化时,所提方法的预测结果也能呈现出良好的预测精度。

Abstract

In order to improve the accuracy of PV power forecast, a short-term PV power forecast model is proposed, which takes into account the multivariate meteorological information from nearby power stations and improves the combined neural network. Firstly, considering the correlation between geographical factors and climatic conditions among adjacent distributed photovoltaic power stations, the grey correlation method is used to determine the main influencing factors of the power stations to be predicted, and the key features of multivariate meteorological information are constituted as the input sequence of the prediction model. Secondly, combining the advantages of the temporal convolutional network （TCN） for efficient extraction of input sequence information and bidirectional gated recurrent unit （BiGRU） for bidirectional data learning, a combined prediction model of TCN-BiGRU is built, and the improved Grey Wolf optimization algorithm （IGWO） is used to optimize the hyperparameters of BiGRU, so as to achieve high-precision prediction of photovoltaic power generation. Finally, the proposed model is verified by the measured data and compared with the similar methods. The results show that combined with multivariate meteorological information, the prediction model can effectively improve the prediction accuracy of power generation in different types of weather throughout the year, and compared with other prediction models, the proposed method can also show good prediction accuracy even when the climate conditions change drastically or randomly.

导出引用

吴伟丽, 米婵, 李磊. 基于多元气象信息和改进组合神经网络的分布式光伏短期功率预测模型[J]. 太阳能学报. 2025, 46(11): 181-192 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1159

Wu Weili, Mi Chan, Li Lei. DISTRIBUTED PHOTOVOLTAIC SHORT-TERM POWER PREDICTION MODEL BASED ON MULTIVARIATE METEOROLOGICAL INFORMATION AND IMPROVED COMBINED NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 181-192 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1159

中图分类号： TM615

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