A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Upgrading Large Existing Buildings to Maximize Their Sustainability
Existing buildings, especially aging ones, often are in urgent need of upgrading to improve their sustainability and environmental performance. Building owners often seek to identify and implement building upgrade measures to improve building sustainability as well as to achieve green certifications such as the leadership in energy and environmental design (LEED) within available budgets. To support building owners and operators as well as building managers in this vital goal, this paper presents two optimization models that are capable of (1) maximizing LEED points for existing buildings within a user-defined upgrade cost, and (2) achieving a user-defined LEED certification level for existing buildings with minimum upgrade cost. These optimization models are developed in two main steps: (1) a formulation step which identifies the model decision variables and formulates objective functions and constraints; and (2) an implementation step which performs the model computations using genetic algorithms and generates results in both tabular and graphical formats. A case study of a large existing building located at University of Colorado Denver was used to illustrate the capabilities of the optimization models and demonstrate their use. The results of the case study showed strong support to building owners and operators to identify the optimal use of their upgrade budgets to achieve LEED certification. The optimization model was designed to generate an action report that can be used by building owners and operators, listing building fixtures and equipment that need to be upgraded, their location in building, expected energy and water reduction, and expected annual savings. The primary contribution of this research is the innovative approach for identifying optimal upgrade measures for large existing buildings to maximize their sustainability using the LEED rating system.
Upgrading Large Existing Buildings to Maximize Their Sustainability
Existing buildings, especially aging ones, often are in urgent need of upgrading to improve their sustainability and environmental performance. Building owners often seek to identify and implement building upgrade measures to improve building sustainability as well as to achieve green certifications such as the leadership in energy and environmental design (LEED) within available budgets. To support building owners and operators as well as building managers in this vital goal, this paper presents two optimization models that are capable of (1) maximizing LEED points for existing buildings within a user-defined upgrade cost, and (2) achieving a user-defined LEED certification level for existing buildings with minimum upgrade cost. These optimization models are developed in two main steps: (1) a formulation step which identifies the model decision variables and formulates objective functions and constraints; and (2) an implementation step which performs the model computations using genetic algorithms and generates results in both tabular and graphical formats. A case study of a large existing building located at University of Colorado Denver was used to illustrate the capabilities of the optimization models and demonstrate their use. The results of the case study showed strong support to building owners and operators to identify the optimal use of their upgrade budgets to achieve LEED certification. The optimization model was designed to generate an action report that can be used by building owners and operators, listing building fixtures and equipment that need to be upgraded, their location in building, expected energy and water reduction, and expected annual savings. The primary contribution of this research is the innovative approach for identifying optimal upgrade measures for large existing buildings to maximize their sustainability using the LEED rating system.
Upgrading Large Existing Buildings to Maximize Their Sustainability
Abdallah, Moatassem (author) / Muddana, Lakshmi (author) / Clevenger, Caroline (author)
2019-12-05
Article (Journal)
Electronic Resource
Unknown
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