基于PCC-LSTM模型的短期负荷预测方法

短期负荷预测是电网合理调度和平稳运行的基础。为提高短期负荷预测精度，提出了一种基于Pearson相关系数(PCC)和长短期记忆(LSTM)神经网络的短期负荷预测方法。该方法运用Pearson相关性分析对原始多维输入变量组成的时间序列进行相关性分析，选取与电力负荷数据相关性较大的影响因素作为输入量，实现原始数据的降维和选优；再通过LSTM神经网络结合Adam优化算法，对与电力负荷相关性较大的影响因素和负荷实际输出序列之间的非线性关系建立网络模型。以嘉捷BOX和重庆丽苑维景国际大酒店的负荷数据作为实际算例，并与Prophet、LSTNet、门控循环(GRU)神经网络模型方法进行对比。结果表明:所提PCC-LSTM模型预测精度均在91%以上，最高可达95.44%，有效提高了负荷预测的精度。 

Short-term load forecasting method based on PCC-LSTM model

Short-term load forecasting is the basis for reasonable dispatch and smooth operation of the power grid. To improve the accuracy of short-term load forecasting, a method based on Pearson correlation coefficient (PCC) and long and short term memory (LSTM) neural network is proposed. This method uses Pearson correlation analysis to analyze the correlation of the time series composed of original multi-dimensional input variables, and selects the influencing factors with greater correlation with the power load data as the input to achieve the dimensionality reduction of the original data. Then, through the combination of LSTM neural network and Adam optimization algorithm, a network model is established for the nonlinear relationship between the influencing factors that have a greater correlation with the power load and the actual output sequence of the load. Taking the load data of Jiajie BOX and Chongqing International Grand Metropark Liyuan Hotel as calculation examples, the proposed model is compared with Prophet, LSTNet, and gated recurrent unit (GRU) models. Results show that the prediction accuracy of the proposed model is above 91%, with the highest up to 95.44%, thus effectively improving the accuracy of load forecasting.