An Effective Metaheuristic Approach for Building Energy Optimization Problems
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abstract
Mathematical optimization can be a useful strategy for minimizing energy usage while designing low-energy buildings. To handle building energy optimization challenges, this study provides an effective hybrid technique based on the pelican optimization algorithm (POA) and the single candidate optimizer (SCO). The suggested hybrid algorithm (POSCO) benefits from both the robust local search power of the single candidate method and the efficient global search capabilities of the pelican optimization. To conduct the building optimization task, the optimization method was developed and integrated with the EnergyPlus codes. The effectiveness of the proposed POSCO method was verified using mathematical test functions, and the outcomes were contrasted with those of conventional POA and other effective optimization techniques. Application of POSCO for global function optimization reveals that, among the thirteen considered functions, the proposed method was best at finding the global solution for seven functions, while providing superior results for the other functions when compared with competitive techniques. The suggested POSCO is applied for reducing an office buildings’ annual energy use. Comparing POSCO to POA procedures, the building energy usage is reduced. Furthermore, POSCO is compared to simple POA and other algorithms, with the results showing that, at specific temperatures and lighting conditions, the POSCO approach outperforms selected state-of-the-art methods and reduces building energy usage. As a result, all data suggests that POSCO is a very promising, dependable, and feasible optimization strategy for dealing with building energy optimization models. Finally, the building energy optimization findings for various climatic conditions demonstrate that the changes to the weather dataset had limited effect on the efficiency of the optimization procedure.
