Multi-objective optimization of embodied and operational energy and carbon emission of a building envelope

Abstract

Buildings significantly contribute to global annual energy consumption and carbon footprint in their operation and initial construction. Coming up with an eco-friendly and energy-efficient building design requires a comprehensive yet complicated optimization that involves a large domain of design measures and indicators due to operational-embodied energy and carbon emissions-energy trade-offs. This paper proposes a multi-phase and multi-objective genetic based framework that couples EnergyPlus to the embodied energy and embodied carbon databases to jointly assess and optimize both the operational and embodied energy and carbon emissions. It determines the most optimal building configuration by adjusting design measures, which encompass geometrical parameters like window-to-wall ratio and building orientation and envelope-related variables such as the construction materials in external walls and window glazing type. This framework is applied to an ASHRAE benchmark model of a small office building located in College Station, Texas, which falls in climate zone 2A. ASHRAE standards 90.1 is considered for creating the initial baseline design. The building is optimized in two phases. In the first phase, the building is optimized for operational and embodied energy to understand potential tradeoffs. In the second phase, the building is optimized to reduce operational and embodied energy as well as carbon emissions simultaneously. This two-phase optimization culminated into two different Pareto front solutions. The results of the first stage of the optimization lead to a 9.73% and a 37.29% reduction in the building's total operational and embodied energy, respectively from the initial baseline design. In the second phase, the building's operational Energy, embodied energy, operational carbon emissions and embodied carbon emissions are reduced by 9.51%, 37.44%, 17.43%, and 35.67%, respectively in comparison with the baseline model.