2019 CSCE Annual Conference - Laval (Greater Montreal)

2019 CSCE Annual Conference - Laval (Greater Montreal) Conference

Optimizing selection of building materials and fixtures to reduce operational costs

Mr. SAMUEL Hutchison, University of Colorado Denver
Mr. Mahdi Ghafoori
Dr. Moatassem Abdallah, University of Colorado, Denver, USA (Presenter)
Dr. Caroline Clevenger, University of Colorado, USA

Buildings generate a considerable amount of greenhouse gases throughout their lifecycle. 80% of energy is typically consumed during the operating phase of the building lifecycle, while the construction and demolishing phases generate only 20%. Furthermore, building typically undergo a number of renovations stages during their life. This can include modifications to fixtures and equipment such as lighting fixtures and HVAC equipment; or building envelope such as windows, glazing, and wall and roof insulation. This paper presents the development of an optimization model that is capable of identifying the optimal selection of building upgrades to minimize building operational cost within a specified upgrade budget. The optimization model is expected to support building owners and their representatives in their ongoing efforts to minimize the operational cost of their buildings where renovation is planned. The optimization model is developed in four main steps. These steps include (1) Identifying decision variables that represent the desired building upgrade measures; (2) formulating objective function to minimize operational cost of existing buildings; (3) modeling all relevant constraints to ensure the practicality of the model results; and (4) implementing the model computations using Genetic Algorithms. The capabilities of the model are demonstrated using a case study of a commercial building. The results of the model showed a 42% reduction in operational costs with an upgrade budget of $125,000. Most of the operational cost savings were attributed to the installation of the photovoltaic system which saved about $2,262 in operating costs annually. The model was run with two other upgrade budgets of $75,000 and $175,000 to show the impacts of the upgrade budget on annual operating costs.