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Open AccessArticle

Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm

1
Eco-System Research Center, Gachon University, Seongnam 13120, Korea
2
Department of Architectural Engineering, Gachon University, Seongnam 13120, Korea
*
Author to whom correspondence should be addressed.
Buildings 2020, 10(11), 195; https://doi.org/10.3390/buildings10110195
Received: 11 September 2020 / Revised: 23 October 2020 / Accepted: 28 October 2020 / Published: 29 October 2020
Optimizing the operating conditions and control set points of the heating, ventilation, and air-conditioning (HVAC) system in a building is one of the most effective ways to save energy and improve the building’s energy performance. Here, we optimized different control variables using a genetic algorithm. We constructed and evaluated three optimal control scenarios (cases) to compare the energy savings of each by varying the setting and number and type of the optimized control variables. Case 1 used only air-side control variables and achieved an energy savings rate of about 5.72%; case 2 used only water-side control variables and achieved an energy savings rate of 16.98%; and case 3, which combined all the control variables, achieved 25.14% energy savings. The energy savings percentages differed depending on the setting and type of the control variables. The results show that, when multiple control set points are optimized simultaneously in an HVAC system, the energy savings efficiency becomes more effective. It was also confirmed that the control characteristics and energy saving rate change depending on the location and number of control variables when optimizing using the same algorithm. View Full-Text
Keywords: HVAC system; optimal control; genetic algorithm HVAC system; optimal control; genetic algorithm
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MDPI and ACS Style

Seong, N.-C.; Kim, J.-H.; Choi, W. Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm. Buildings 2020, 10, 195. https://doi.org/10.3390/buildings10110195

AMA Style

Seong N-C, Kim J-H, Choi W. Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm. Buildings. 2020; 10(11):195. https://doi.org/10.3390/buildings10110195

Chicago/Turabian Style

Seong, Nam-Chul; Kim, Jee-Heon; Choi, Wonchang. 2020. "Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm" Buildings 10, no. 11: 195. https://doi.org/10.3390/buildings10110195

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