神经网络短期光伏发电预测的应用研究进展

贾凌云; 云斯宁; 赵泽妮; 李红莲; 王赏玉; 杨柳

doi:10.19912/j.0254-0096.tynxb.2021-0501

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (12) : 88-97. DOI: 10.19912/j.0254-0096.tynxb.2021-0501

神经网络短期光伏发电预测的应用研究进展

贾凌云¹, 云斯宁¹, 赵泽妮¹, 李红莲², 王赏玉³, 杨柳³

作者信息 +

RECENT PROGRESS OF SHORT-TERM FORECASTING OF PHOTOVOLTAIC GENERATION BASED ON ARTIFICIAL NEURAL NETWORKS

Jia Lingyun¹, Yun Sining¹, Zhao Zeni¹, Li Honglian², Wang Shangyu³, Yang Liu³

Author information +

文章历史 +

摘要

准确的太阳能发电功率短期预测是保证电力调度和大规模光伏并网的关键。该文对近年来光伏发电功率短期预测研究进展进行综述,并对影响光伏发电功率的各种气象因素进行相关性分析。针对用于光伏发电短期功率预测的人工神经网络模型和深度学习模型进行总结和评述。太阳辐照度是影响预测模型精度的主要气象参数。在光伏发电功率短期预测中,神经网络及其组合模型均表现出较好的预测精度,但组合模型整体上优于单一预测模型。

Abstract

Accurate short-term forecasting of photovoltaic generation is crucial to ensure power dispatching and large-scale photovoltaic grid connection. This review paper presents an extensive review on recent progress in the short-term forecasting of solar power generation. The correlation analysis of various meteorological factors affecting on solar power generation is implemented. The artificial neural network models and deep learning models for solar power forecasting are summarized and reviewed. The solar irradiance is the main meteorological parameter affecting the accuracy of forecasting models. In the short-term forecasting of solar power generation, both neural network models and hybrid models demonstrate a satisfactory prediction accuracy, whereas the hybrid models perform better than the single forecasting models in the prediction accuracy.

导出引用

贾凌云, 云斯宁, 赵泽妮, 李红莲, 王赏玉, 杨柳. 神经网络短期光伏发电预测的应用研究进展[J]. 太阳能学报. 2022, 43(12): 88-97 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0501

Jia Lingyun, Yun Sining, Zhao Zeni, Li Honglian, Wang Shangyu, Yang Liu. RECENT PROGRESS OF SHORT-TERM FORECASTING OF PHOTOVOLTAIC GENERATION BASED ON ARTIFICIAL NEURAL NETWORKS[J]. Acta Energiae Solaris Sinica. 2022, 43(12): 88-97 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0501

中图分类号： TM615

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