An AI-driven model for predicting and optimizing energy-efficient building envelopes.

Unlike many previous studies that often focus on optimizing energy efficiency for buildings when detailed design drawings are available, this paper introduces a newly integrated model for energy-efficient building envelope design in the early stages (when detailed design drawings are not yet available). The newly developed model includes three main components: a simulation model, a predictive model, and an optimization model. The simulation model simulates the building's energy performance, considering different values for various envelope parameters. The predictive model employs machine learning algorithms, including RF, ANN, DNN, SVM, GENLIN, and GB (in which GB has been identified as the most suitable algorithm), boasting a very high R 2 (0.994) to assess energy consumption. The optimization model which uses AI optimization algorithms (such as NSGA II, DSE, and MOPSO) integrates the machine learning predictive model into the evaluation function during the evolutionary process, efficiently searching for Pareto-optimal building envelope solutions. Results show simultaneous savings in cost and energy, with savings of 7.52 % in cost and 8.48 % in energy, or 21.17 % in cost and 0.4 % in energy, for a case study in Vietnam. This model establishes a foundation by providing design solutions for stakeholders to assess, and can incorporate additional objectives at later stages. [ABSTRACT FROM AUTHOR]