Multi-Objective Optimization Methodology for Net Zero Energy Buildings

Abstract : The challenge in Net Zero Energy Building (NZEB) design is to find the best combination of design strategies that will face the energy performance problems of a particular building. This paper presents a methodology for the simulation-based multi-criteria optimization of NZEBs. Its main features include four steps: building simulation, optimization process, multi-criteria decision making (MCDM) and testing solution's robustness. The methodology is applied to investigate the cost-effectiveness potential for optimizing the design of NZEBs in different case studies taken as diverse climatic zones in Lebanon and France. The investigated design parameters include: external walls and roof insulation thickness, windows glazing type, cooling and heating set points, and window to wall ratio. Furthermore, the inspected RE systems include: solar domestic hot water (SDHW) and photovoltaic (PV) array. The proposed methodology is a useful tool to enhance NZEBs design and to facilitate decision making in early phases of building design. Specifically, the non-dominated sorting genetic algorithm (NSGA-II) is chosen in order to minimize thermal, electrical demands and life cycle cost (LCC) while reaching the net zero energy balance; thus getting the Pareto-front. A ranking decision making technique Elimination and Choice Expressing the Reality (ELECTRE III) is applied to the Pareto-front so as to obtain one optimal solution.
Type de document :
Article dans une revue
Journal of Building Engineering, Elsevier, 2018, 16, pp. 57-71. 〈10.1016/j.jobe.2017.12.003〉
Liste complète des métadonnées

https://hal-clermont-univ.archives-ouvertes.fr/hal-01668846
Contributeur : Pascal Biwole <>
Soumis le : mercredi 20 décembre 2017 - 12:07:47
Dernière modification le : mardi 27 mars 2018 - 16:06:22

Identifiants

Citation

Fatima Harkouss, Farouk Fardoun, Pascal Henry Biwole. Multi-Objective Optimization Methodology for Net Zero Energy Buildings. Journal of Building Engineering, Elsevier, 2018, 16, pp. 57-71. 〈10.1016/j.jobe.2017.12.003〉. 〈hal-01668846〉

Partager

Métriques

Consultations de la notice

296