BiLSTM Multitask Learning-Based Combined Load Forecasting Considering the Loads Coupling Relationship for Multienergy System

TLDR

Accurate load forecasting is key to economic dispatch and efficient operation of Multi‑Energy Systems (MES). The study proposes a combined load forecasting method for MES using BiLSTM multi‑task learning. The method models multi‑load coupling by selecting features via the Maximum Information Coefficient and applying a BiLSTM multi‑task learning framework that jointly forecasts cooling, heating, and electric loads, thereby sharing coupling information. Case studies confirm the method’s superior learning speed and forecasting accuracy.

Abstract

Accurate load forecasting is the key to economic dispatch and efficient operation of Multi-Energy System (MES). This paper proposes a combined load forecasting method for MES based on Bi-directional Long Short-Term Memory (BiLSTM) multi-task learning. Firstly, this paper investigates the multi-energy interaction mechanism and multi-loads characteristics and analyzes the correlation of multi-loads in different seasons. Then, a combined load forecasting method is proposed, which focuses on making full use of the coupling relationship among multiple loads. In the forecasting model, the different loads are selected combinedly as the input features according to the Maximum Information Coefficient (MIC). The multi-task learning is adopted to construct the cooling, heating and electric combined load forecasting model based on the BiLSTM algorithm, which can effectively share the coupling information among the loads. Finally, case studies verify the effectiveness and superiority of the proposed method in both learning speed and forecasting accuracy.