Computer and Modernization ›› 2024, Vol. 0 ›› Issue (12): 59-65.doi: 10.3969/j.issn.1006-2475.2024.12.009

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Short-term Load Forecasting in Industrial Parks with Multi-scale Time Coding

  

  1. (School of Computer Science, Xi’an Polytechnic University/Shaanxi Key Laboratory of Clothing Intelligence, Xi’an 710048, China) 
  • Online:2024-12-31 Published:2024-12-31

Abstract:  To enhance the accuracy of short-term load prediction in industrial parks, a model based on complete ensemble empirical 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 detect 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.

Key words: short-term load forecasting in industrial parks, complete ensemble empirical mode decomposition with adaptive noise, auto-encoder, feature fusion, Transformer

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