Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II)
•We use a procedure to support the design of a nearly zero-energy house in the Mediterranean climate.•We use optimization software to minimize long-term thermal and visual discomfort.•Programming codes have been developed to calculate long-term thermal discomfort indices.•General design good practic...
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| Vydáno v: | Energy and buildings Ročník 104; s. 378 - 394 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.10.2015
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| Témata: | |
| ISSN: | 0378-7788 |
| On-line přístup: | Získat plný text |
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| Abstract | •We use a procedure to support the design of a nearly zero-energy house in the Mediterranean climate.•We use optimization software to minimize long-term thermal and visual discomfort.•Programming codes have been developed to calculate long-term thermal discomfort indices.•General design good practices are derived from the optimization outcome.•The complexity of the solution space requires the adoption of advanced simulation techniques.
Multi-objective optimization methods provide a valid support to buildings’ design. They aim at identifying the most promising building variants on the basis of diverse and potentially contrasting needs. However, optimization has been mainly used to optimize the energy performance of buildings, giving secondary importance to thermal comfort and usually neglecting visual comfort and the indoor air quality.
The present study addresses the design of a detached net zero-energy house located in Southern Italy to minimize thermal and visual discomfort. The optimization problem admits four objective functions (thermal discomfort during winter and summer and visual discomfort due to glare and an inappropriate quantity of daylight) and uses the non-dominated sorting genetic algorithm, implemented in the GenOpt optimization engine through the Java genetic algorithms package, to instruct the EnergyPlus simulation engine.
The simulation outcome is a four-dimensional solution set. The building variants of the Pareto frontier adopt diverse and non-intuitive design alternatives. To derive good design practices, two-dimensional projections of the solution set were also analyzed. Finally, in cases of complex optimization problems with many objective functions, optimization techniques are recommended to effectively explore the large number of available building variants in a relatively short time and, hence, identify viable non-intuitive solutions. |
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| AbstractList | •We use a procedure to support the design of a nearly zero-energy house in the Mediterranean climate.•We use optimization software to minimize long-term thermal and visual discomfort.•Programming codes have been developed to calculate long-term thermal discomfort indices.•General design good practices are derived from the optimization outcome.•The complexity of the solution space requires the adoption of advanced simulation techniques.
Multi-objective optimization methods provide a valid support to buildings’ design. They aim at identifying the most promising building variants on the basis of diverse and potentially contrasting needs. However, optimization has been mainly used to optimize the energy performance of buildings, giving secondary importance to thermal comfort and usually neglecting visual comfort and the indoor air quality.
The present study addresses the design of a detached net zero-energy house located in Southern Italy to minimize thermal and visual discomfort. The optimization problem admits four objective functions (thermal discomfort during winter and summer and visual discomfort due to glare and an inappropriate quantity of daylight) and uses the non-dominated sorting genetic algorithm, implemented in the GenOpt optimization engine through the Java genetic algorithms package, to instruct the EnergyPlus simulation engine.
The simulation outcome is a four-dimensional solution set. The building variants of the Pareto frontier adopt diverse and non-intuitive design alternatives. To derive good design practices, two-dimensional projections of the solution set were also analyzed. Finally, in cases of complex optimization problems with many objective functions, optimization techniques are recommended to effectively explore the large number of available building variants in a relatively short time and, hence, identify viable non-intuitive solutions. |
| Author | Cattarin, Giulio Pagliano, Lorenzo Carlucci, Salvatore Causone, Francesco |
| Author_xml | – sequence: 1 givenname: Salvatore surname: Carlucci fullname: Carlucci, Salvatore email: salvatore.carlucci@ntnu.no organization: NTNU Norwegian University of Science and Technology, Department of Civil and Transport Engineering, Trondheim, Norway – sequence: 2 givenname: Giulio surname: Cattarin fullname: Cattarin, Giulio organization: eERG, end-use Efficiency Research Group, Department of Energy, Politecnico di Milano, Milano, Italy – sequence: 3 givenname: Francesco surname: Causone fullname: Causone, Francesco organization: eERG, end-use Efficiency Research Group, Department of Energy, Politecnico di Milano, Milano, Italy – sequence: 4 givenname: Lorenzo surname: Pagliano fullname: Pagliano, Lorenzo organization: eERG, end-use Efficiency Research Group, Department of Energy, Politecnico di Milano, Milano, Italy |
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| Keywords | NSGA-II Visual comfort LPD PPD CO2 NW Zero energy buildings JGAP SHC HVAC SE LBNL UDI ANSI Thermal comfort IEA SW DGI EN PID DGP ACR EU Genetic algorithm NE LD PMV ALD Multi-objective optimization nZEB PCM Simulation-based optimization ASHRAE |
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| SubjectTerms | DGI Genetic algorithm LPD Multi-objective optimization NSGA-II Simulation-based optimization Thermal comfort UDI Visual comfort Zero energy buildings |
| Title | Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II) |
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