Deep learning-based multivariate load forecasting for integrated energy systems

Abstract With the continuous development of integrated energy utilization technology and the diversification of users’ energy demand, and the existing single load forecasting method is difficult to deal with the complex coupling relationship derived between various types of loads, resulting in the inaccuracy of multivariate load forecasting, which makes the accurate forecasting of multivariate loads of integrated energy systems more challenging. To address the aforementioned issues, we propose a short-term forecasting method for integrated energy multivariate loads based on GRU-MTL. Firstly, we conduct a correlation analysis using the hierarchical analysis method and Copula theory, and select the model input features based on the final correlation metric results. Secondly, we construct a multivariate load forecasting model for electricity, cooling, and heating based on gated cyclic unit and multi-task learning. Finally, a comparison was made with the traditional model, and the results indicate that the constructed model has better predictive accuracy and is more efficient in terms of time.