基于时间戳特征提取和CatBoost-LSTM模型的光伏短期发电功率预测

徐恒山; 莫汝乔; 薛飞; 秦子健; 潘鹏程

doi:10.19912/j.0254-0096.tynxb.2023-0007

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (5) : 565-575. DOI: 10.19912/j.0254-0096.tynxb.2023-0007

基于时间戳特征提取和CatBoost-LSTM模型的光伏短期发电功率预测

徐恒山¹, 莫汝乔¹, 薛飞², 秦子健³, 潘鹏程¹

作者信息 +

SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON TIMESTAMP FEATURE EXTRATION AND CatBoost-LSTM MODEL

Xu Hengshan¹, Mo Ruqiao¹, Xue Fei², Qin Zijian³, Pan Pengcheng¹

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文章历史 +

摘要

为解决预测模型输入特征维度不足以及单一模型预测精度不高而导致的短期功率预测效果较差的问题,提出一种对时间戳进行特征提取（FE）的CatBoost和长短期记忆（LSTM）神经网络组合的光伏短期发电功率预测模型。首先,利用信息熵加权的方式对传统灰色关联分析进行改进,并采用改进方法对辐照度、温度、降雨量等气象特征与发电功率特征进行关联性分析,选择关键特征作为输入特征;然后,从时间戳和功率特征中提取年、月、日、时、分、秒、时间戳-功率等新时序特征;在此基础上,将关键气象特征与提取的新时序特征用于组合模型训练;最后,利用光伏电站的真实运行数据对所提方法和组合模型进行算例分析。结果表明：提取的新时序特征和组合模型均有助于提高预测精度,在非晴天工况下组合模型的预测误差较单一模型可降低12~23个百分点,且与其他组合模型相比具有更高的预测精度。

Abstract

To solve the problem that the short-term power prediction effect is poor due to the insufficient input feature dimensions of the prediction model and the low prediction accuracy of a single model, a short-term prediction model of photovoltaic power generation combining with timestamp feature extraction and based on CatBoost and long short-term memory （LSTM） neural network is proposed. Firstly, the traditional grey correlation analysis is improved by using the information entropy weighting, and the improved method is used to analyze the correlation between the meteorological features such as irradiance, temperature, rainfall and power, and select the key features as the input features; Then, new temporal features such as year, month, day, hour, minute, second, timestamp-power are extracted from timestamp and power; On this basis, key meteorological features and extracted new temporal features are used for combined model training; Finally, an example analysis was conducted on the proposed method and combination model using real operating data of photovoltaic power plants. The results show that both the extracted new time series feature and the combined model can help improve the prediction accuracy. Under the non-sunny conditions, the prediction error of the combined model can be reduced by about 12%-23% compared with the single model, and it has higher prediction accuracy compared with other combined models.

导出引用

徐恒山, 莫汝乔, 薛飞, 秦子健, 潘鹏程. 基于时间戳特征提取和CatBoost-LSTM模型的光伏短期发电功率预测[J]. 太阳能学报. 2024, 45(5): 565-575 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0007

Xu Hengshan, Mo Ruqiao, Xue Fei, Qin Zijian, Pan Pengcheng. SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON TIMESTAMP FEATURE EXTRATION AND CatBoost-LSTM MODEL[J]. Acta Energiae Solaris Sinica. 2024, 45(5): 565-575 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0007

中图分类号： TM615

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