Machine Learning for Sustainable Building Design: Energy, Emissions & Comfort
This study investigates the application of six machine learning regression models to predict building performance in a residential unit located in Sari, Iran. Using a calibrated EnergyPlus model and three years of utility data, the research evaluates primary energy consumption, emissions, indoor air quality, thermal comfort, and visual discomfort. The aim is to enhance sustainable design decision-making by comparing the efficiency and accuracy of different models. Machine Learning Models in Building Performance The research evaluates Random Forest, K-Nearest Neighbors, Support Vector Regression, Artificial Neural Network, Extreme Gradient Boosting, and Linear Regression. Each model is tested against real-world performance indicators, highlighting their predictive strength and weaknesses in handling multidimensional building datasets. Dataset and Methodology A synthetic dataset of 1,826 configurations with 25 input variables was developed using EnergyPlus. The dataset was split in...