基于长短时记忆网络和轻梯度增强机的风电功率多步预测

Multi-Step Prediction of Wind Power Based on Long Short-Term Memory Network and Light Gradient Boosting Machine

风力发电预测在电力系统规划和决策中具有重要意义。然而,由于气象事件的随机性,准确预测风力功率一直是一个具有挑战性的难题。针对此,提出一种基于长短时记忆网络(long short term memory,LSTM)-轻梯度增强机(light gradient boosting machine,LGBM)的预测模型,结合LSTM和LGBM的优势,以提高对未来风力功率的短期预测能力。LSTM模型捕捉风力功率的时序模式和趋势,并生成一个包含序列信息的隐藏状态,LGBM模型作为LSTM模型的补充,通过接收LSTM提取的隐藏状态作为输入,进一步预测未来的风力功率。实验结果表明,提出的LSTM-LGBM模型在全局训练时优于其他模型,证明了LSTM的时序特征提取能力及LGBM的预测性能。该模型的应用有助于提高风力发电预测的准确性,并为电力系统的运行和资源分配提供有效的支持。

Wind power forecasting is important in power system planning and decision-making.However,accurately predicting wind power has always been a challenging problem due to the randomness of meteorological events.To solve this problem,this paper proposes a prediction model based on long short term memory(LSTM)-light gradient boosting machine(LGBM),which combines the advantages of LSTM and LGBM to improve the short-term prediction ability of future wind power.In this paper,the LSTM model is used to capture the timing patterns and trends of wind power and generate a hidden state containing sequence information,and the LGBM model is used as a supplement to the LSTM model to further predict future wind power by receiving the hidden state extracted by the LSTM as input.Experimental results show that the proposed LSTM-LGBM model is superior to other models in global training,which proves the temporal feature extraction ability of LSTM and the predictive performance of LGBM.The application of this model helps to improve the accuracy of wind power generation forecasts and provide effective support for the operation and resource allocation of power systems.