Multi-objective optimization of building envelope components based on economic, environmental, and thermal comfort criteria

The process of determining the appropriate systems to be replaced as part of a building renovation can pose significant challenges in the decision-making phase. These challenges often include financial restrictions, thermal comfort maintenance, and environmental mitigation. Forecasting these factors will therefore enable the design of more efficient and sustainable buildings. This study proposes a simulation-based multi-objective optimization methodology to explore the optimal building envelope design according to economic, environmental, and thermal comfort criteria. The retrofit evaluation and optimization approach developed is applied to a classroom building, and the investigation is carried out in six different climates corresponding to six locations in Morocco. The considered design features are the decision variables related to the building envelope components and are derived from a preliminary analysis conducted using a bioclimatic chart implemented in the Climate Consultant 6.0 environmental program for six locations. The selected design variables include walls and roof compositions, window glazing type, windows-to-wall ratio (WWR), and window shading. Three objective functions are considered to be minimized in this work, including life cycle cost (LCC), life cycle CO2 (LCCO2), and thermal discomfort hours (Tdh). The dynamic building simulation software TRNSYS and the free generic optimization tool GENOPT are coupled to accomplish the multi-objective optimization approach. Simulation results revealed that there is a unique set of values of optimal solutions for each climate zone. Based on the evaluated trade-offs between different criteria, optimum solutions lead to potential reductions of 18-14 % in life cycle cost saving index (LCCSI), 20-26 % in life cycle CO2 saving index (LCCO2SI), and 21-11 % in thermal discomfort hours saving index (TdhSI) according to the climate type. Moreover, implementing the optimum solutions leads to a payback period ranging from 6 to 8.5 years depending on climate type.