An approach for making optimal decisions in building energy efficiency retrofit projects


ŞENEL SOLMAZ A., HALICIOĞLU F. H., Gunhan S.

INDOOR AND BUILT ENVIRONMENT, cilt.27, sa.3, ss.348-368, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 3
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1177/1420326x16674764
  • Dergi Adı: INDOOR AND BUILT ENVIRONMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.348-368
  • Anahtar Kelimeler: Decision support methods, Sensitivity analysis, Multi-objective optimization, Building performance simulation, Building energy modelling, Building energy efficiency retrofit, WEIGHTED SUM METHOD, SENSITIVITY-ANALYSIS, MULTIOBJECTIVE OPTIMIZATION, PERFORMANCE, UNCERTAINTY, METHODOLOGY, SIMULATION, DESIGN, TOOL
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

The study presents an optimization-based decision support approach to determine the optimal energy efficiency retrofit options in existing buildings. The main challenge encountered in the decision-making process of building retrofit projects is the selection of an optimal set of solutions within a wide range of solution space according to multiple criteria. In order to overcome this problem, this study uses an integrated optimization approach by combining both the variance-based sensitivity analysis and optimization methods to maximize energy savings and optimize financial returns in building energy efficiency retrofit projects. The proposed approach was applied to an existing public school building in Izmir, Turkey that represents hot-humid climate to test its validity and performance. The optimizations were performed and results were obtained for three different scenarios whose content was determined based on project objectives and constraints fulfilled by a survey conducted by building users. The case study results indicate that the proposed approach is effective in defining the application order of retrofit options by identifying the most influential parameters for building energy efficiency and finding out the optimal retrofit solutions per multiple criteria.