AbstractBuilding’s energy and water consumption and associated utility costs can be significantly reduced if proper energy and water-efficiency upgrades are applied. Due to the limitations of the upgrade budgets and the availability of a wide range of energy and water-efficiency measures, decision makers are always faced with a challenging task to identify optimal sets of building upgrade measures. This paper presents the development of a new model that is capable of identifying an optimum selection of building upgrade measures to minimize equivalent annual cost (EAC) while maintaining the performance of existing buildings and complying with available upgrade budgets. The model is developed in three main phases: (1) formulation phase, where decision variables, objective function, and constraints are identified and formulated; (2) implementation phase that performs the model computations and specifies the model input and output data; and (3) performance evaluation phase where two case studies of university buildings are analyzed to demonstrate the capabilities of the model. Furthermore, a case study from the literature is used to verify the model performance and document its new capabilities. The primary contributions that this research adds to the body of knowledge are: (1) developing new model that can identify optimal selection of building upgrades while complying with user-specified requirements for building operational performance and available upgrade budgets in short computation time, (2) modeling a wide range of upgrade measures for building fixtures and equipment as well as envelope components, and (3) integrating updatable databases from vendors and suppliers to generate up-to-date and practical recommendations. The model is designed to use OpenStudio to analyze combinations of feasible alternatives such as HVAC systems, wall and roof insulations, window types, and water heaters to calculate the corresponding building energy consumption. The model is designed to perform its computations using binary linear programming to identify global optimum solutions in short computational time. The results of the case studies illustrated the capabilities of the model in optimizing building upgrade measures to reduce the building utility costs up to 38% while complying with specified upgrade budgets.

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