Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm

Nasruddin, Sholahudin, Pujo Satrio, Teuku Meurah Indra Mahlia, Niccolo Giannetti, Kiyoshi Saito

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)48-57
Number of pages10
JournalSustainable Energy Technologies and Assessments
Volume35
DOIs
Publication statusPublished - Oct 2019

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Air conditioning
Thermal comfort
Energy utilization
Genetic algorithms
Neural networks
Heating
Thermostats
Multiobjective optimization
Air
Thermal energy
Cooling systems

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

Cite this

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title = "Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm",
abstract = "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.",
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Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm. / Nasruddin; Sholahudin; Satrio, Pujo; Mahlia, Teuku Meurah Indra; Giannetti, Niccolo; Saito, Kiyoshi.

In: Sustainable Energy Technologies and Assessments, Vol. 35, 10.2019, p. 48-57.

Research output: Contribution to journalArticle

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