Occupant's thermal comfort augmentation and thermal load reduction in a typical residential building using genetic algorithm

Abstract

The uncontrollable rise in energy consumption become a most significant issue in recent decades. One of the largest consumers of energy resources across all industries is the residential building sector. Researchers have suggested several strategies to reduce energy loss, including enclosing insulation in wall structures because air conditioning systems account for the majority of energy use inside homes. The main goal of this article is to increase residents' thermal comfort (Tc) while reducing their heating load (HL) and cooling load (CL). Using the EnergyPlus program, the building model was simulated in sample cities with various climatic conditions. For optimization, the first seven design variables were determined in Jeplus software and then multi-objective optimization was performed by the Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm. As a result, Tc, HL, and CL values improved by 38–62, 61 to 100, and 17 to 39 percent, respectively.

Keywords

• Insulation
• Multi-objective-optimization
• Heating and cooling setpoint temperature
• Heating load
• Cooling load
• Thermal comfort