Short-term Load Forecasting in Industrial Parks with Multi-scale Time Coding
To enhance the accuracy of short-term load prediction in industrial parks,a model based on complete ensemble em-pirical mode decomposition with adaptive noise with auto-encoder and convolutional neural network-Transformer is proposed.The model addresses the issues of coupling,nonlinearity,and stochasticity of short-term loads.Given that sudden events and emergencies in real scenarios can cause abnormal fluctuations in load data,the sliding time window method is used to firstly de-tect and correct any anomaly data.Secondly,the frequency domain decomposition algorithm is utilized to resolve the coupling of the load data by dividing the historical load data into multi-scale frequency domain components.Thirdly exogenous features with high correlation to be selected load fluctuations are generated using auto-encoder and feature engineering methods and used as inputs along with the components.Then a convolutional neural network is used to analyze latent features and fuse them with the inputs.The results are fed into the Transformer network,which combines its coding capability and multi-attention mechanism to capture the characteristics of the time series.The final prediction result is obtained by super-imposing the final output of each sub-module.Using the real load dataset as an example,the results demonstrate that the proposed model significantly enhances short-term load forecasting accuracy.
short-term load forecasting in industrial parkscomplete ensemble empirical mode decomposition with adaptive noiseauto-encoderfeature fusionTransformer
万方数据
维普
