李春平, 张沛, 彭春华, 尹瑞, 时珉. 基于随差遗忘长短期记忆的风电功率实时预测[J]. 现代电力, 2021, 38(1): 110-118. DOI: 10.19725/j.cnki.1007-2322.2020.0200
引用本文: 李春平, 张沛, 彭春华, 尹瑞, 时珉. 基于随差遗忘长短期记忆的风电功率实时预测[J]. 现代电力, 2021, 38(1): 110-118. DOI: 10.19725/j.cnki.1007-2322.2020.0200
LI Chunping, ZHANG Pei, PENG Chunhua, YIN Rui, SHI Min. Real-time Prediction of Wind Power Based on Error Following Forget Gate-based Long Short-term Memory[J]. Modern Electric Power, 2021, 38(1): 110-118. DOI: 10.19725/j.cnki.1007-2322.2020.0200
Citation: LI Chunping, ZHANG Pei, PENG Chunhua, YIN Rui, SHI Min. Real-time Prediction of Wind Power Based on Error Following Forget Gate-based Long Short-term Memory[J]. Modern Electric Power, 2021, 38(1): 110-118. DOI: 10.19725/j.cnki.1007-2322.2020.0200

基于随差遗忘长短期记忆的风电功率实时预测

Real-time Prediction of Wind Power Based on Error Following Forget Gate-based Long Short-term Memory

  • 摘要: 由于标准长短期记忆(long short-term memory, LSTM)遗忘门更新方式不能实时反映预测误差对模型预测的修正作用,提出随差遗忘长短期记忆(Error Following Forget Gate-based LSTM, EFFG-based LSTM)的风电功率实时预测模型。用上一时刻的风电功率预测值与实际值的误差来更新遗忘门,从而降低上一时刻预测误差对此时风电功率预测精度的影响,提升风电功率滚动预测精度,并采用某实际风电场的历史风电功率数据和数值预报气象数据进行了验证,结果表明:基于EFFG-based LSTM网络风电功率实时预测模型预测值的均方根误差小于3%,满足系统调度相关要求;准确率、合格率达到90%以上,比基于支持向量机和标准LSTM模型具有更高的预测精度。

     

    Abstract: Because of the fact that the update mode of the forget gate of standard long short-term memory (abbr. LSTM) could not reflect the correction of predicated error to the model based prediction value in real time, a real-time wind power prediction model utilizing error following forget gate (abbr. EFFG)-based LSTM was proposed. The error between the predicted value of wind power and the actual value at the previous moment was used to update the forget gate, thus the influence of the predicted error of the wind power at the previous moment on the prediction accuracy at current moment could be reduced, and the rolling prediction accuracy of wind power could be improved. The historical wind power data of a certain actual wind farm and the numerical forecasted meteorological data were utilized to verify the proposed model. Verification results show that the root-mean-square error of the value predicted by the proposed real-time wind power prediction model based on EFFG-based LSTM is less than 3%, and both accuracy and acceptability reach more than 90%, thus such results are better than the prediction accuracy by the models based on support vector machine and standard LSTM model.

     

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