| 神经网络短期光伏发电预测的应用研究进展 |
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| 引用本文： | 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. |
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| 作者姓名： | 贾凌云 云斯宁 赵泽妮 李红莲 王赏玉 杨柳 |
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| 作者单位： | 1.西安建筑科技大学材料科学与工程学院,西安 710055; 2.西安建筑科技大学信息与控制工程学院,西安 710055; 3.西安建筑科技大学建筑学院,西安 710055 |
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| 基金项目： | 国家重点研发计划（2018YFB1502902） |
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| 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。
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| 关 键 词： | 光伏发电 神经网络 预测 深度学习 相关性 |
| 收稿时间： | 2021-05-11 |
RECENT PROGRESS OF SHORT-TERM FORECASTING OF PHOTOVOLTAIC GENERATION BASED ON ARTIFICIAL NEURAL NETWORKS |
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| 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. |
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| Authors: | Jia Lingyun Yun Sining Zhao Zeni Li Honglian Wang Shangyu Yang Liu |
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| Affiliation: | 1. School of Materials Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China; 2. School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China; 3. School of Architecture, Xi’an University of Architecture and Technology, Xi’an 710055, China |
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| 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. |
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| Keywords: | photovoltaic power neural networks forecasting deep learning correlation |
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