An optimization framework for building energy retrofits decision-making. (April 2017)

• Record Type: Journal Article
• Authors: Jafari, Amirhosein; Valentin, Vanessa
• Is Part Of: Building and environment. Volume 115(2017)
• Page Start: 118
• Page End: 129
• Publication Date: 2017-04
• Subjects: Energy retrofits -- Decision-making -- Life-cycle cost -- Optimization model -- Residential building; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2017.01.020
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Buildings are major consumers of energy in the United States. One way to improve building's energy efficiency is through energy retrofitting. The selection of a combination of retrofitting measures for a specific building is a complex process. Despite of the numerous resources that provide advice on how to retrofit a facility, the study of important variables affecting this decision remains limited. Further research is needed on the development of decision-making models to select the optimum energy retrofitting strategy in order to maximize energy retrofitting benefits. This study proposes a decision-making framework that: (1) calculates the economic benefits of energy retrofitting in terms of reduction of life-cycle cost for a specific building during its service life; (2) determines the optimum retrofitting budget that minimizes the total life-cycle cost of the building during its service-life; and (3) selects the optimum energy retrofitting strategy (among available energy retrofitting measures) to maximize the homeowner economic benefits during service-life of the building based on available investments. This study contributes to the body of knowledge in three aspects: (1) considering a comprehensive economic objective for decision-making in energy retrofits that includes majority of cost-related components of building life-cycle cost; (2) introducing a novel simplified energy prediction method by integrating dynamic and static modeling; and (3) incorporating energy retrofitting decision-making uncertainties to reach more accurate results. In order to demonstrate the implementation of the framework, a case study exercise of a house built in 1960's in Albuquerque, New Mexico is used. Highlights: A decision-making model to select the best energy retrofitting strategy is proposed. It calculates the economic benefits of energy retrofits in terms of reduction in LCC. It helps homeowners to plan or evaluate retrofitting strategies effectively.