Decision-making model for optimum energy retrofitting strategies in residential buildings

Abstract

Existing buildings consume an inordinate amount of energy globally. The consumption negatively affects the environment and economy; therefore, it is necessary to improve energy performance in buildings by retrofitting the existing ones. Several energy retrofitting strategies are proposed in the energy efficiency programs, and hence, the selection of optimal and efficient solutions is a complex task. Several factors affect the implementation of energy retrofitting measures such as budget, comfort requirement, and reliable economic value. To this end, the study was conducted to develop a decision-support model for residential building owners based on Mixed Integer Linear Programming. The model was designed to select optimum energy retrofitting strategies. The objective function involves minimizing the total Life Cycle Cost of the energy retrofitting plan to achieve economic benefit while considering the budget, thermal comfort, and recommended illumination level as primary constraints. To test the validity of the model, a case study of a residential building was conducted. The findings of the study suggest an energy retrofitting plan that decreases approximately 70% of energy consumption. A sensitivity analysis was conducted to examine the effect of the change in the cost component on the Life Cycle Cost and energy saving.