In the United States, over 60% of the housing inventory is more than 30 years old. One way to improve energy efficiency of those aged buildings is through housing retrofits. One of the main challenges of housing retrofit projects is making the decision about the amount of investment that results in maximum long-time benefits. In terms of life-cycle cost for a housing retrofit, different factors may affect the type of retrofitting alternatives to be implemented in the project. This research first introduces an optimization model for decision-making in housing retrofit. The model incorporates the use of genetic algorithm for selecting the optimum retrofitting plan based on the minimum life-cycle cost of the building. Then using a case study through this research a sensitivity analysis is performed to evaluate the impact of different factors such as service life of the building, homeowner's available budget, and discount rate of the building location on the suggested optimum retrofitting alternatives. The initial results illustrate that the retrofitting efforts could be more feasible when the service life of the building is high and the amount of discount rate is low. The results can help homeowners to make a more accurate decision for a housing energy retrofit.
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