Short-time multi-energy load forecasting method based on CNN-Seq2Seq model with attention mechanism

Abstract

Integrated Energy Systems have become a vital energy utilization to alleviate the multiple stress of energy, environment, and economy worldwide. Integrated Energy Microgrid (IEM) is a small-scale integrated energy system located in a distribution network close to the demand side. The accurate forecasting of multi-load is an essential prerequisite for ensuring the reliable and economic operation of an IEM. Comprehensively considering temperature, humidity, wind speed, and the coupling relationship of multi-energy, this paper proposes a CNN-Seq2Seq model with an attention mechanism based on a multi-task learning method for a short-time multi-energy load forecasting. In detail, CNN is used to extract useful features of the input data. Then, the short-time multi-energy load is forecasted by using Seq2Seq according to the extracted features. Meanwhile, the attention mechanism and multi-task learning method are introduced to improve the accuracy of load forecasting. The simulation results with the actual data of an IEM validate the effectiveness of the proposed short-time multi-energy load forecasting method.