Optimal Selection of Sustainability Measures to Minimize Building Operational Costs
Publication: Construction Research Congress 2014: Construction in a Global Network
Abstract
Buildings are responsible for 40% of the national annual energy consumption, 72% of electricity consumption, 13% of water consumption, 39% of all carbon footprint, and 30% of the total waste output in the United States. These negative environmental and economic effects have motivated many owners to explore various sustainability measures in their buildings, including energy efficient devices (such as LED and fluorescent lighting, HVAC systems, ground-source heat pumps, hand dryers, vending machines, refrigerators, and water heaters); renewable energy equipment (such as solar panels and wind turbines); and water saving plumbing fixtures (such as efficient faucets, urinals, and toilets). These sustainability measures can improve building performance in terms of energy efficiency, water conservation, waste reduction, and longer life cycle. Despite higher initial costs, in most cases, these sustainability measures can pay back these costs because of their lower energy and water consumption. Because of the large combination of options and the payback period, decision makers often are faced with the challenges of which option to select because of limited budgets. This paper presents an optimization model that is designed to identify an optimal selection of building sustainability measures that are capable of minimizing the annual operational costs of the building while complying with a limited upgrade budget. The optimization model analyzes the replacement of building fixtures and equipment with more energy- and water-efficient alternatives to reduce building operational costs. The optimization analysis accounts for the initial replacement cost, maintenance cost, operational costs, energy and water savings, discount rate, payback period, and functional performance. An application example of a public building is used to illustrate the capabilities of the developed optimization model and verify its results.
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© 2014 American Society of Civil Engineers.
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Published online: May 13, 2014
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