A GA-based system sizing method for net-zero energy buildings considering multi-criteria performance requirements under parameter uncertainties

Net-zero energy buildings (NZEBs) are considered as an effective solution to current environmental and energy problems. To achieve expected performance, system sizes in a NZEB must be properly selected. Parameter uncertainties have been proved to have significant impacts on system sizing and need to be systematically considered. Due to complex uncertainty impacts, proper system sizing in a NZEB with multi-criteria performance is always a real challenge. To deal with the challenge, this study presents a genetic algorithm-GA based system sizing method for NZEBs. Taking users’ multi-criteria performance requirements as constraints, the proposed method aims to minimize total system initial costs by selecting proper sizes of five different systems under uncertainties. The five systems include an air-conditioning system, photovoltaic panels, wind turbines, a thermal energy storage system and an electrical energy storage system. The performance requirements come from three diverse criteria which are zero energy, thermal comfort and grid independence. Using real weather data of 20 years in Hong Kong, the case studies demonstrate the effectiveness of the proposed method in selecting proper system sizes corresponding to user specified performance requirements. In addition, the results indicate conventional descriptions of parameter uncertainties need to be improved for better system sizing of NZEBs.