基于K-Medoids聚类与栅格法提取负荷曲线特征的CNN-LSTM短期负荷预测

CNN-LSTM short-term load forecasting based on the K-Medoids clustering and grid method to extract load curve features

高效准确的短期负荷预测是电力系统安全稳定与经济运行的重要保障。针对峰荷与谷荷预测误差较大的问题,提出一种基于栅格法提取负荷曲线特征的卷积神经网络和长短期记忆网络(convolutional neural network and long short term memory network,CNN-LSTM)混合预测模型。首先,采用K-Medoids算法对日负荷曲线聚类,将各聚类中心作为典型代表日负荷曲线。采用栅格法将典型代表日负荷曲线划分为若干个区间并依次编号,提取负荷曲线的特征。然后,将各典型代表日负荷曲线特征与对应负荷类型历史数据重构成新的特征集输入到CNN-LSTM混合神经网络中。利用CNN挖掘数据间的特征形成新的特征向量,再将该特征向量输入到LSTM中进行预测。最后,以美国新英格兰地区2012至2013年电力负荷数据集为例进行仿真验证。结果表明,所提方法在不同日期下的负荷预测精度均有所提升,并且在提升日负荷平均预测精度的同时,有效提升了峰荷、谷荷的预测精度。

Efficient and accurate short-term load forecasting is an important guarantee of safe,stable and economic operation of a power system.Given the large prediction errors of peak and valley loads,this paper proposes a convolutional neural network and long short term memory network(CNN-LSTM)hybrid prediction model based on a grid method to extract load curve features.First,the K-Medoids algorithm is used to cluster the daily load curves,and each cluster center is taken as the typical daily load curve.The typical daily load curve is divided into several sections by the grid method and numbered successively to extract the features of the load curve.Then,the characteristics of each typical daily load curve and the historical data of corresponding load types are reconstructed into a new feature set and input into the CNN-LSTM hybrid neural network.The features among the data are mined by the CNN to form a new feature vector,which is then input into LSTM for prediction.Finally,the 2012-2013 power load data set in New England is taken as an example for simulation verification.The results show that the load prediction accuracy of the proposed method is improved for different dates,and the prediction accuracy of peak and valley loads is effectively improved while the average forecast accuracy of the daily load is also improved.