Minimizing Energy Consumption and Carbon Emissions of Aging Buildings. (2015)

• Record Type: Journal Article
• Authors: Abdallah, Moatassem; El-Rayes, Khaled; Clevenger, Caroline
• Is Part Of: Procedia engineering. Volume 118(2015)
• Page Start: 886
• Page End: 893
• Publication Date: 2015
• Subjects: Energy Consumption -- Sustainability Measures -- Aging Buildings -- Carbon emissions; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2015.08.527
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: The building sector in the United States is responsible for 41% of energy consumption and 39% of carbon footprint while the majority of energy consumption and carbon footprint are caused by aging buildings which represent 70% of existing buildings in the United States. The energy consumption of aging buildings can be significantly reduced by identifying and implementing green building upgrade measures based on available budgets. Aging buildings are often in urgent need for upgrading to improve their operational, economic, and environmental performance. This paper presents the development of an optimization model that is capable of identifying the optimal selection of building upgrade measures to minimize energy consumption of aging buildings while complying with limited upgrade budgets and building operational performance. This optimization model is designed to estimate building energy consumption using energy simulation software packages such as eQuest and it is integrated with databases of building products. This optimization model performs analysis of replacing existing building fixtures and equipment during the optimization computations to identify the optimal replacement of building products that minimizes building energy consumption and carbon emissions. The model is designed to provide detailed results for building owners and operators, which include specifications for the recommended upgrade measures and their location in the building; upgrade cost; expected energy, operational, and life-cycle cost savings; and expected payback period. This paper illustrates the new and unique capabilities of the developed optimization model.