Performance Analysis of Multi-Source Heat Pumps: A Regression-Based Approach to Energy Performance Estimation

The growing demand for energy-efficient heating, ventilation, and air conditioning (HVAC) systems has increased interest in multi-source heat pumps as a sustainable solution. While extensive research has been conducted on heat pump performance prediction, there is still a lack of practical tools for early-stage system evaluation. This study addresses that gap by developing regression-based models to estimate the performance of various heat pump configurations, including air-source, ground-source, and dual-source systems. A simplified performance estimation model was created, capable of delivering results with accuracy levels comparable to TRNSYS simulation outputs, making it a valuable and accessible tool for system evaluation. The analysis was conducted across nine climatic zones in Italy, considering key environmental factors such as air temperature, ground temperature, and solar irradiance. Among the tested configurations, hybrid systems like Solar-Assisted Ground-Source Heat Pumps (SAGSHP) achieved the highest performance, with SCOP values up to 4.68 in Palermo and SEER values up to 5.33 in Milan. Regression analysis confirmed strong predictive accuracy (R-2 = 0.80-0.95) and statistical significance (p < 0.05), emphasizing the models' reliability across different configurations and climatic conditions. By offering easy-to-use regression formulas, this study enables engineers and policymakers to estimate heat pump performance without relying on complex simulations.