基于AM-LSTM模型的超短期风电功率预测

近年来,中国的风力发电产业高速发展。然而风力发电具有不稳定性,风电功率超短期预测结果的准确性直接影响到电网安全有效的运行。为了进一步提高风电功率超短期预测的精确度,提出了长短期记忆网络-注意力模型(AM-LSTM)风电功率预测模型,该模型将长短期记忆网络(long-term and short-term memory,LSTM)和注意力模型(attention model,AM)相结合, LSTM网络能够处理好风速、风向等时间序列变量与风电功率之间的非线性关系,注意力模型能够优化LSTM网络的权重,从而使预测结果更加准确。采用真实的风电场历史数据进行实验,结果表明:提出的AM-LSTM预测模型能够有效利用多变量时间序列数据进行风电场发电功率的超短期预测,比传统的BP神经网络和LSTM网络具有更精确的预测效果。该预测模型为风电场地电力调度提供了科学参考。

Ultra-short-term Wind Power Prediction Based on AM-LSTM Model

In recent years, China''s wind power industry has developed rapidly. However, wind power has instability, and the accuracy of ultra-short-term wind power prediction results directly affects the safe and efficient operation of the power grid. In order to further improve the accuracy of wind power ultra-short term prediction, a wind power prediction based on AM-LSTM model was proposed. This model combines long-term and short-term memory network (LSTM) with attention model (AM). In combination, the LSTM network can handle the nonlinear relationship between time series variables such as wind speed and wind direction and wind power, and the attention model can optimize the weight of the LSTM network to make the prediction result more accurate. Experiments using real wind farm historical data show that the proposed AM-LSTM prediction model can effectively utilize multivariate time series data for ultra-short-term prediction of wind farm power generation, which is more accurate than traditional BP neural networks and LSTM networks forecast effect.