基于天气融合和LSTM网络的分布式光伏短期功率预测方法

doi:10.11930/j.issn.1004-9649.202104057

摘要/Abstract

摘要： 分布式光伏发电功率高精度预测对配电网安全稳定运行有重要意义。针对分布式光伏发电设备的功率预测问题，基于天气信息和深度学习方法提出了一种分布式光伏短期功率预测方法。首先将天气进行分类融合，实现训练集的全面覆盖；然后基于长短期记忆网络（long short-term memory，LSTM）深度学习方法构建分布式光伏短期功率预测模型；最后实现分布式光伏功率预测。

关键词: 分布式光伏, 光伏发电预测, 深度学习, 短期预测

Abstract: The high-precision prediction of distributed photovoltaic power generation is of great significance to the safe and stable operation of the distribution network. In this paper, based on weather information and depth learning method, a short-term power prediction method for distributed photovoltaic power generation equipment is proposed. First, classify and fuse the weather to achieve full coverage of the training set. Then, build a distributed photovoltaic short-term power prediction model based on the long short-term memory (LSTM) deep learning network. Finally, realize distributed photovoltaic power prediction.

Key words: distributed photovoltaic, photovoltaic power generation forecast, deep learning, short-term forecasting

李丰君, 王磊, 赵健, 张建宾, 张世尧, 田杨阳. 基于天气融合和LSTM网络的分布式光伏短期功率预测方法[J]. 中国电力, 2022, 55(11): 149-154.

LI Fengjun, WANG Lei, ZHAO Jian, ZHANG Jianbin, ZHANG Shiyao, TIAN Yangyang. Research on Distributed Photovoltaic Short-Term Power Prediction Method Based on Weather Fusion and LSTM-Net[J]. Electric Power, 2022, 55(11): 149-154.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202104057

https://www.electricpower.com.cn/CN/Y2022/V55/I11/149

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