基于计算智能的电力数据智能分析及应用研究

Research on Intelligent Analysis and Application of Power Data Based on Computational Intelligence

为了提升智能电网负荷预测准确率,提出了一种基于深度学习的短期电力负荷预测模型。在长短时记忆网络和卷积神经网络基础上,构建混合CNN-LSTM预测模型结构。利用基于叠加卷积降噪自动编码器对电力数据进行特征提取,提出包含2个堆叠的LSTM层和1个线性输出层的负荷预测模型。24 h短期负荷预测结果表明,所提模型MAE、RMSE、MAPE和R2指标分别为232.08、292.19、0.0322、0.909,与XGBoost模型相比,性能分别提升74.8%、73.8%、70.8%和10.9%。

In order to improve the accuracy of smart grid load forecasting,a short-term power load forecasting model based on deep learning is proposed.On the basis of the long-short term memory network and convolutional neural network,a hybrid CNN-LSTM prediction model structure is constructed.The automatic encoder based on superposition convolution noise reduction is used to extract the features of power data,and a load forecasting model with two stacked LSTM layers and a linear output layer is proposed.The 24 h short-term load forecasting results show that the MAE,RMSE,MAPE and R2 indicators of the proposed model are 232.08,292.19,0.0322 and 0.909,respectively,and the performance is improved by 74.8%,73.8%,70.8%and 10.9%,respectively,compared with XG Boost model.