基于集成LSTM模型的数据驱动需求预测

Data-driven demand prediction based on integrated LSTM model

电力用户参与电网调度能够有效提升电网灵活性,但其行为的不确定性限制了需求响应的发展。针对此问题,文中首先构建激励型需求响应的实现框架,阐述负荷聚合商(LA)如何整合需求侧资源参与电力市场业务,并将用户随激励政策进行响应的行为转换为需求弹性。然后,基于长短时记忆(LSTM)算法,提出一种集成LSTM的数据驱动的需求弹性预测方法,同时为提升预测模型性能,对源数据进行平滑与缩放处理,并增加损失函数权重系数。算例结果表明,与传统LSTM算法及k近邻预测法相比,文中所提预测方法用于用户需求弹性预测时平均预测误差分别降低了5.33%和28.8%,用于总负荷预测时平均绝对百分比误差(MAPE)分别降低了2.06%和3.09%。同时文中基于集成LSTM分析了平滑、缩放数据预处理对预测精度的影响,结果表明对原始数据进行预处理可有效提升预测精度。

The flexibility of the power grid can be significantly promoted by the participation of power customers in dispatch.However,as the uncertainty of customer behavior,the development of demand response services is limited.To solve this problem,the framework of incentive-based demand response is constructed in this paper.The way that load aggregators integrate demand-side resources to participate in the power market is elaborated.And the behavior of power customers responding to incentive policies is transformed into demand elasticity.Then,a data-driven demand elasticity prediction method based on the integrated long short-term memory(LSTM)is proposed.Meanwhile,to improve the performance of the prediction model,the original data is smoothed and scaled,and the weight coefficients of the loss function are added.The simulation results show that,compared with the traditional LSTM algorithm and the k-proximity prediction method,the average forecasting error with the proposed model for the demand elasticity is reduced by 5.33%and 28.8%,and mean absolute percentage error(MAPE)for the total load prediction is reduced by 2.06%and 3.09%.Additionally,based on integrated LSTM,the influence of smoothing and scaling data preprocessing on prediction accuracy is analyzed.The results show that the prediction accuracy can be significantly promoted by data preprocessing.