Multimodal modeling framework and characteristic analysis of a new air-source heat pump heating system.

Heating systems that combine air-source heat pumps with phase-change energy storage tanks can effectively utilize off-peak electricity and enhance energy storage efficiency. However, in actual industrial building heating operations, the system's thermal characteristics are influenced by multiple factors, making it challenging for traditional single-factor prediction models to accurately capture their dynamic behavior. To address this issue, this paper develops a novel prediction model called XMS (XGBoost, MLP, and Stacking model). First, heating data from an operational air-source heat pump system coupled with a phase-change energy storage tank are collected. Second, the XMS model is proposed, based on a stacking ensemble strategy. It integrates XGBoost and a multi-layer perceptron (MLP) as base learners and employs a meta-learner to perform secondary modeling of their outputs. Finally, the XMS model is compared with a traditional MLP model. The results indicate that the XMS model demonstrates significantly superior predictive performance compared to the MLP model, achieving a 22.5% reduction in root mean square error (RMSE) and a 10.1% increase in the coefficient of determination (R²). The XMS model better captures the nonlinear and multivariate coupling characteristics of the heating system. This study provides an efficient and reliable modeling method for load forecasting and control optimization in air-source heat pump phase-change energy storage heating systems, offering valuable guidance for intelligent heating control technology.