基于EMD-LSTM的光伏发电预测模型
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TM744

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国家重点基础研究发展计划(973计划)资助项目(2016YFB0900100);上海市科委重大项目(18DZ1100303)


Photovoltaic power generation prediction model based on EMD-LSTM
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Project Supported by National Key Research Program of China (2016YFB0900100)

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    摘要:

    随着能源消费结构的改变,可再生能源发电的消纳比例逐渐上升。文中以光伏发电功率为研究对象,分析了不同天气状态下的发电功率曲线特性及不同气象因素与光伏发电出力的相关性,进而提出了一种经验模态分解-长短期记忆神经网络(EMD-LSTM)方法融合的光伏发电功率预测模型。首先对预处理后的光伏发电功率历史序列进行重构,并对重构后的出力序列进行EMD分解,针对分解得到的各子序列分别建立长短期记忆神经网络模型,最后将各子序列预测模型得到的结果叠加得到光伏发电功率预测值。采用国内某地区光伏发电的实际出力数据对模型进行了检验,与滑动平均自回归模型(ARIMA)、支持向量机模型(SVM)、LSTM等预测模型相比,文中所提出的模型预测误差小,能有效提高光伏发电功率的预测精度。

    Abstract:

    With the change of energy consumption, the consumption proportion of renewable energy power generation rises gradually. Photovoltaic power is chosen as the research object to study the characteristics of power curve under different weather conditions and to analyze the correlation of the efforts of various meteorological factors and photovoltaic power generation. Then by combining Empirical Mode Decomposition (EMD) and Long-short Term Memory (LSTM), a load forecasting model suitable for photovoltaic power generation is proposed. Firstly, the historical sequence of preprocessed photovoltaic power generation is reconstructed. Then, the reconstructed historical time series is decomposed in EMD model, and the decomposed sub-sequences are respectively predicted in the LSTM network. Finally, the results of each sub-sequence are superimposed to obtain the predicted result of photovoltaic power generation. The actual output data of photovoltaic power generation in a certain region in China is used to test the model. Compared with the ARIMA model, SVM model, LSTM model and EMD-SVM model, the model proposed in this paper has lower prediction error and can effectively improve the prediction accuracy of photovoltaic power generation.

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引用本文

朱玥,顾洁,孟璐.基于EMD-LSTM的光伏发电预测模型[J].电力工程技术,2020,39(2):51-58

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  • 收稿日期:2019-09-06
  • 最后修改日期:2019-10-22
  • 录用日期:2020-03-05
  • 在线发布日期: 2020-04-13
  • 出版日期: 2020-03-28
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