Simulation-Based Multi-Objective Optimization of institutional building renovation considering energy consumption, Life-Cycle Cost and Life-Cycle Assessment

Abstract

Buildings are responsible for a significant amount of energy consumption resulting in a considerable negative environmental impact. Therefore, it is essential to decrease their energy consumption by improving the design of new buildings or renovating existing buildings. Heat losses or gains through building envelopes affect the energy use and the indoor condition. Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems are responsible for 33% and 25% of the total energy consumption in office buildings, respectively. However, renovating building envelopes and energy consuming systems to lessen energy losses is usually expensive and has a long payback period. Despite the significant contribution of research on optimizing energy consumption, there is limited research focusing on the renovation of existing buildings to minimize their Life Cycle Cost (LCC) and environmental impact using Life Cycle Assessment (LCA). This paper aims to find the optimal scenario for the renovation of institutional buildings considering energy consumption and LCA while providing an efficient method to deal with the limited renovation budget. Different scenarios can be compared in a building renovation strategy to improve energy efficiency. Each scenario considers several methods including the improvement of the building envelopes, HVAC and lighting systems. However, some of these scenarios could be inconsistent and should be eliminated. Another consideration in this research is the appropriate coupling of renovation scenarios. For example, the HVAC system must be redesigned when renovating the building envelope to account for the reduced energy demand and to avoid undesirable side effects. A genetic algorithm (GA), coupled with an energy simulation tool, is used for simultaneously minimizing the energy consumption, LCC, and environmental impact of a building. A case study is developed to demonstrate the feasibility of the proposed method.