TY - JOUR
T1 - Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm
AU - Nasruddin,
AU - Sholahudin,
AU - Satrio, Pujo
AU - Mahlia, Teuku Meurah Indra
AU - Giannetti, Niccolo
AU - Saito, Kiyoshi
N1 - Funding Information:
The authors would like to thank DRPM Universitas Indonesia for Hibah Publikasi Artikel di Jurnal Internasional Kuartil Q1 dan Q2 (Q1Q2) Tahun Anggaran 2019 Grant No. NKB-0316/UN2.R3.1/HKP.05.00/2019. We acknowledge also the support provided by PT Holcim Indonesia, Tbk and ATMI Cikarang for their permission to conduct the measurement and Ms. Sasha Media who helped us in collecting data.
Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/10/1
Y1 - 2019/10/1
N2 - The optimization of heating, ventilating and air conditioning (HVAC) system operations and other building parameters intended to minimize annual energy consumption and maximize the thermal comfort is presented in this paper. The combination of artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) is applied to optimize the two-chiller system operation in a building. The HVAC system installed in the building integrates radiant cooling system, variable air volume (VAV) chiller system, and dedicated outdoor air system (DOAS). Several parameters including thermostat setting, passive solar design, and chiller operation control are considered as decision variables. Subsequently, the percentage of people dissatisfied (PPD) and annual building energy consumption is chosen as objective functions. Multi-objective optimization is employed to optimize the system with two objective functions. As the result, ANN performed a good correlation between decision variables and the objective function. Moreover, MOGA successfully provides several alternative possible design variables to achieve optimum system in terms of thermal comfort and annual energy consumption. In conclusion, the optimization that considers two objectives shows the best result regarding thermal comfort and energy consumption compared to base case design.
AB - The optimization of heating, ventilating and air conditioning (HVAC) system operations and other building parameters intended to minimize annual energy consumption and maximize the thermal comfort is presented in this paper. The combination of artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) is applied to optimize the two-chiller system operation in a building. The HVAC system installed in the building integrates radiant cooling system, variable air volume (VAV) chiller system, and dedicated outdoor air system (DOAS). Several parameters including thermostat setting, passive solar design, and chiller operation control are considered as decision variables. Subsequently, the percentage of people dissatisfied (PPD) and annual building energy consumption is chosen as objective functions. Multi-objective optimization is employed to optimize the system with two objective functions. As the result, ANN performed a good correlation between decision variables and the objective function. Moreover, MOGA successfully provides several alternative possible design variables to achieve optimum system in terms of thermal comfort and annual energy consumption. In conclusion, the optimization that considers two objectives shows the best result regarding thermal comfort and energy consumption compared to base case design.
KW - Building optimization
KW - Genetic algorithm
KW - Neural network
KW - Radiant cooling
UR - http://www.scopus.com/inward/record.url?scp=85067884492&partnerID=8YFLogxK
U2 - 10.1016/j.seta.2019.06.002
DO - 10.1016/j.seta.2019.06.002
M3 - Article
AN - SCOPUS:85067884492
SN - 2213-1388
VL - 35
SP - 48
EP - 57
JO - Sustainable Energy Technologies and Assessments
JF - Sustainable Energy Technologies and Assessments
ER -
