Performance improvement of solar-assisted hybrid water-source heat pumps through multi-objective system control optimization

초록

Hybrid heat pumps show substantial potential for improving building energy efficiency; however, their control strategies remain limited because of complex interactions among system variables. This study presents a multiobjective control optimization framework for a solar-assisted hybrid water-source heat pump (HWSHP). Artificial neural network metamodels have been developed to accurately predict the start-up and operating power consumption. Non-dominated sorting genetic algorithm II combined with the technique for order preference by similarity to ideal solution is utilized for determining the optimal control solutions. The optimized control logic substantially improves the energy performance of the HWSHP compared with that of baseline control, achieving reductions in power consumption, operating costs, and CO2 emissions of up to 31.6 %, 33.7 %, and 33.7 %, respectively. In addition, the lifecycle costs and payback period of the HWSHP decrease by 13 % and 1.9 years, respectively. These results underscore the efficacy of control-oriented optimization in substantially improving the energy, economic, and environmental performances of HWSHP systems.

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