A Multi-Energy Load Forecasting Framework for Integrated Energy Systems With Climate Resilience

Abstract

This paper proposes an MTL-TCN-Transformer model based on multi-task learning in response to the challenges including climate change faced by multi-load forecasting in integrated energy systems. To tackle the issues in multi-load forecasting, such as the increased volatility due to climate change and the complex coupling relationships among loads, the proposed model is designed with an integrated architecture that effectively extracts long-term temporal dependencies, captures correlations between loads and establishes global dependencies. By using the maximum information coefficient method for feature screening, nine features representing the correlation between loads, such as the ratio and difference between loads, were constructed to capture the complex correlations among multiple loads. The application of an adaptive weight update strategy suppresses fluctuations during the training process, enhancing the robustness and convergence speed of the model. The experimental results show that the model proposed in this paper exhibits better performance than the traditional methods in the four-season multi-load forecasting, with an average reduction of 67.952% in the MAPE of the electrical load. This paper provides a solution for high-precision multi-element load forecasting and points out future expansion directions such as enhancing cross-regional generalisation capabilities.