Optimization of a solar thermal heating system using an integrated RSM-entropy weight method

The promotion of clean heating in rural northern China is hindered by the high costs and performance limitations of existing systems. Most optimization studies rely on one-dimensional objectives or subjective weighting methods, leading to biased and non-generalizable designs. To address this, we propose an integrated Response Surface Methodology (RSM)-Entropy Weight Method (EWM) framework for the multi-objective optimization of a dual-tank solar-electric heating system. The EWM objectively assigns weights to energy, economic, and environmental (3E) indicators based on data dispersion, revealing environmental benefit (36.8%) as the most influential factor. Using RSM, we model and optimize four key parameters: collector tilt angle, collector area, and the volumes of solar storage and heating tanks. The optimal configuration (tilt: 43.52°, area: 59.96 m 2 , storage tank: 2.70 m 3 , heating tank: 2.92 m 3 ) yields a comprehensive system score (CSS) of 0.9386, significantly outperforming the baseline. This study provides not only a specific optimal design but also a robust, objective, and reusable methodological framework, advancing the optimization of rural heating systems from single-dimensional improvement to multi-dimensional synergy.