An artificial intelligence framework for predicting operational energy consumption in office buildings

This research delves into the intricate dynamics of energy consumption in buildings, using data-driven modeling with machine learning (ML) algorithms to optimize design choices for heightened energy efficiency. A substantial dataset, comprising 66,800 operational energy records from office buildings in 40 cities worldwide representing 10 distinct climate conditions, was analyzed using four ML algorithms: Random Forest Regressor, Extra Trees Regressor, Gradient Boosting Regressor (GBR), and eXtreme Gradient Boosting Regressor (XGBR). Notably, the developed GBR and XGBR models demonstrated superior precision in predicting energy use intensity during the testing phase, achieving a mean absolute percentage error of less than 1.2 % and a coefficient of determination of more than 0.99. The findings highlighted the significant impact of building shape and temperature on annual operational energy consumption, providing valuable insights for energy-efficient design optimization. Finally, the grey wolf optimizer was applied to identify optimal design parameters for various scenarios, laying the groundwork for designing energy-efficient buildings in cities with diverse meteorological patterns.