Deepening Intelligent Microgrid Management: A Study on Improving Load Forecasting Accuracy Based on Informer Models

Abstract

In the context of the “double carbon” strategy and the rapid development of deep learning, it provides new ideas for load forecasting of intelligent microgrids. In this study, we choose the Informer model based on the Transformer framework, which improves the self-attention mechanism and reduces the computational cost, to improve load accuracy and to achieve intelligent management of the microgrid system by accurately forecasting power load data.