Multiobjective Optimization Model for Maximizing Sustainability of Existing Buildings

Abstract

Aging buildings in the United States represent 70% of existing buildings, and they are often in urgent need of upgrades to improve their operational, economic, and environmental performance. Recent studies reported the need for and significance of improving the sustainability of existing buildings to stabilize and reduce their greenhouse gas emissions and minimize their negative environmental impacts. This can be accomplished by integrating sustainable upgrade measures in existing buildings to improve their energy efficiency, water consumption, material recycling, waste reduction, lifecycle, and indoor environment. These upgrade measures include energy-efficient lighting and HVAC systems, renewable energy systems, water-saving plumbing fixtures, and sustainable management of building solid waste. Decision makers often need to identify an optimal set of these upgrade measures capable of maximizing the sustainability of their buildings while complying with limited upgrade budgets and building functional requirements. To support decision makers in this critical and challenging task, this paper presents the development of a multiobjective optimization model for maximizing the sustainability of existing buildings. The optimization model is designed to generate optimal trade-offs among the three sustainability objectives of (1) minimizing building negative environmental impacts that include greenhouse gas emissions, refrigerant impacts, mercury-vapor emissions, light pollution, and water consumption; (2) minimizing building upgrade cost; and (3) maximizing the number of earned points of the Leadership in Energy and Environmental Design rating system for existing buildings (LEED-EB). The computations of the developed model are performed using a nondominated sorting genetic algorithm (NSGAII) because of its capability of handling multiobjective optimization problems and nonlinearity and step changes in the model objective functions and constraints. The model performance was evaluated using a case study of an existing public building, and the results illustrated the unique and practical capabilities of the developed model in generating optimal trade-offs among the previously mentioned three optimization objectives. These capabilities are expected to support building owners and facility managers in their ongoing efforts to achieve green building certification and to promote the use of cost-effective green upgrade measures in existing buildings.