Short-term load forecasting of integrated energy system based on improved complete ensemble empirical mode decomposition with adaptive noise maximum information coefficient transformer

As an important research direction for the future innovation and development of the energy industry, integrated energy system (IES) requires short-term load forecasting as the decision-making basis. A short-term load forecasting model based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) maximum information coefficient (MIC) transformer is proposed to address the problem of high volatility and strong randomness in IES short-term load. The model effectively reduces the complexity of time series and alleviates the impact of modal aliasing by ICEEMDAN. Perform correlation analysis and reconstruction of the decomposed intrinsic mode function using MIC to improve the coupling relationship between multivariate loads. Finally, Transformer model based on self-attention mechanism is used to predict each component and obtain the final prediction result. The horizontal comparison experiment and vertical ablation experiment were conducted on the model using the IES dataset from Arizona State University in the United States. The results showed that the predictive performance of the model was improved to a certain extent and it had a certain degree of application prospects.