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Multimodal modeling framework and characteristic analysis of a new air-source heat pump heating system
Mingzhi Jiang1,2, Guohui Feng3, Zhiwei Wang1,2
1School of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang, 110168, China.
Scientific Reports
|June 3, 2026
Summary
A new XMS model, combining XGBoost, MLP, and stacking, accurately predicts industrial heating systems. This advanced model improves energy storage efficiency and offers reliable guidance for intelligent heating control.
Area of Science:
- Energy Engineering
- Artificial Intelligence in Building Systems
Background:
- Air-source heat pumps with phase-change energy storage tanks utilize off-peak electricity but face prediction challenges due to complex thermal dynamics.
- Traditional single-factor models struggle to capture the dynamic behavior of these integrated heating systems.
Purpose of the Study:
- To develop a novel, accurate prediction model for industrial building heating systems integrating air-source heat pumps and phase-change energy storage.
- To enhance the efficiency and reliability of load forecasting and control optimization for these systems.
Main Methods:
- Collected operational heating data from an air-source heat pump system with a phase-change energy storage tank.
- Developed a stacking ensemble model (XMS) integrating XGBoost and Multi-Layer Perceptron (MLP) as base learners, with a meta-learner for secondary modeling.
Main Results:
- The XMS model demonstrated superior predictive performance over a traditional MLP model.
- Achieved a 22.5% reduction in root mean square error (RMSE) and a 10.1% increase in the coefficient of determination (R²).
- The XMS model effectively captures the nonlinear and multivariate coupling characteristics of the heating system.
Conclusions:
- The proposed XMS model provides an efficient and reliable method for predicting the performance of air-source heat pump phase-change energy storage heating systems.
- Offers valuable guidance for intelligent heating control technology and optimization of energy storage efficiency.
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