Driving Data into Energy-Efficient Buildings

摘要

The building sector accounts for over a quarter of energy-related CO2 emissions. Due to the numerous factors that dictate a building's energy demands over time, new modeling approaches are necessary to plan and design for sustainability at scale. Recently in Applied Energy, three independent articles have demonstrated machine-learning-augmented models to forecast building energy demand at high accuracy and computational efficacy.