Globally, a large proportion of a building's energy is consumed to achieve thermal comfort, as promoting energy efficiency and environmental sustainability of buildings is an important global goal. Therefore, the new building regulations are geared explicitly toward energy-saving projects. However, there are usually many competitive priorities, such as maximizing energy use in office buildings, leading to the complex multi-objective optimization topic. The main objective of this research was to reduce the energy consumed for cooling load in the office buildings in the city of Cairo, which is located in a hot desert climate. This was achieved by optimizing ten different design variables with DesignBuilder (version 6.1) and applying thermal comfort with the Thermal Comfort CBE tool based on the ASHRAE Standard55 system.
Using the regression method, a sensitivity analysis (SA) of 10 design variables was used to assess their effect on both cooling and thermal comfort loads. The variables were divided into two groups according to their importance, where the genetic algorithm (GA) was applied to the group of high importance to reach the optimal solution for the two goals that represent the problem of the office building in the study; after confirming the adoption of the high importance group, the improvement is still considered to determine the most appropriate values. Therefore, a comparison has been made of three potential solutions for the highly important variables to determine if any additional reduction in cooling loads and thermal comfort can be achieved to choose the optimal solution that will be applied and analyzed in the office building to achieve the two goals separately, first (reduce the cooling load) and to reduce annual energy consumption and secondly (reduce the number of hours of thermal discomfort) to reach an optimized human thermal comfort.
Office buildings, Genetic algorithm, Energy, Cooling load, Thermal comfort, Sensitivity analysis, Optimization
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Arafa, Somaia Mohamed; Elazab, Esraa Mohamed; and Elsayyad, Nanees Abd Elhamid
"Genetic Algorithm Theory in Simulation Programs as A Tool for Improving the Environmental Performance of Office Buildings,"
Mansoura Engineering Journal: Vol. 48
, Article 17.
Available at: https://doi.org/10.58491/2735-4202.3033