Abstract
• TRNSYS software is to simulate the solar assisted air source heat pump system . • The solar fraction design values of solar water heating systems are not economically optimal in China. • Long-term economic optimization of solar fraction design values for solar assisted air source heat pump systems is performed. • An ANN-generated proxy model is to predict the most economical solar fraction design values of solar assisted air source heat pump systems. The solar assisted air source heat pump (SAASHP) system is widely used for campus water heating owing to its cost-effectiveness and cleanliness. In the current design process of the SAASHP system, the solar collector area is determined by the solar fraction ( f ). However, it is difficult to design a cost-optimal f that can comprehensively take the atmospheric factors, device model, electricity price and initial investment into consideration. To address this issue, a SAASHP system that meets the bathing hot water demand of 480 students was established by TRNSYS software, and the cost-optimal solar collector areas and f values for 12 cities across 4 different solar resource regions in China were obtained by the TRNOPT tool of TRNSYS. Moreover, the effects of SC price, electricity price, bathing hot water demand and rated COP of ASHP on cost-optimal f values were investigated by sensitivity analysis. Finally, a proxy model was generated by the artificial neural network toolbox of MATLAB to predict the cost-optimal f values for different scenarios. The results showed that compared to the economically optimized SAASHP system, the designed SAASHP system according to the national design standards for solar heating systems in China has a larger SC area value, leading to an increase in the annual life cycle cost of the system. The difference between the optimized and designed values of solar collector area and f for the SAASHP system was minor in rich resource and sub-rich resource regions but was larger in general resource and poor resource regions. The solar collector price, electricity price, bathing hot water demand and the rated COP of the SAHP were significant factors affecting the annual life cycle cost, the cost-optimal f and solar collector area of the SAASHP system. The proxy model generated by the artificial neural network toolbox of MATLAB can accurately predict the economically optimal solar collector area and f values for SAASHP systems in different cities and system factors, providing a method for the economic design of SAASHP systems in China.
Published Version
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