Optimizing the selection of building upgrade measures to minimize the operational negative environmental impacts of existing buildings

The negative environmental impacts of existing buildings can be significantly reduced by implementing various green upgrade measures to minimize their greenhouse gas (GHG) emissions, refrigerant impacts, mercury-vapor emissions, lighting pollution, and water use. This paper presents the development of a novel optimization model that provides the capability of optimizing building upgrade decisions in order to minimize their negative environmental impacts while complying with a specified upgrade budget and owner-specified building operational performance. The model is developed in three main phases: metrics identification phase that develops metrics to quantify the negative environmental impacts of existing buildings; formulation phase that formulates the model decision variables, objective function, and constrains; and implementation phase that performs the optimization computations and specifies the model input and output data. A case study of a rest area building is analyzed to evaluate the performance of the developed model and illustrate its new capabilities. The results of analyzing the case study demonstrated the capabilities of the model in optimizing building upgrade measures to minimize the building negative environmental impacts while complying with the specified upgrade budget and the building operational performance.