Simulation-based optimization of heating and cooling seasonal performances of an air-to-air heat pump considering operating and design parameters using genetic algorithm

In this study, a novel simulation model is developed to optimize the seasonal coefficient of performance (SCOP) and seasonal energy efficiency ratio (SEER) of air-to-air heat pumps under various operating and design parameters. The developed heat exchanger model is simplified using artificial neural networks. The operating and design parameters were optimized to maximize the SEER and SCOP according to the outdoor temperature using the genetic algorithm. The considered operating and design parameters included the compressor frequency, indoor air flow rate, outdoor air flow rate, peak load, and compressor volume. The SCOP and SEER with the optimization of all three operating parameters were 7.0% and 21.4% higher than those with the optimization of the compressor frequency, respectively. In addition, the maximum designed cooling peak load, which satisfied the SEER greater than 8.5, was 3.7 kW. Moreover, the maximum SCOP was observed at the designed heating peak load of 3.8 kW. Further, as the compressor volume increases by 37.2% and 22.8% over the baseline compressor volume, the SCOP and SEER increase by 3.8% and 1.1%, respectively.