Leveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages: A Preliminary Application

Leveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages: A Preliminary Application

The need for energy efficiency in neighborhood-scale architectural design is driven by environmental imperatives and escalating energy costs. This study identifies three key phases in a design process framework where machine learning can be applied to optimize energy consumption in early design stag...

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Bibliographic Details
Main Authors: Andrea Giuseppe di Stefano, Matteo Ruta, Gabriele Masera, Simi Hoque
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Buildings
Subjects:
predictive analysis
energy efficiency strategies
data-driven neighborhood design
design process framework
urban building energy modeling
Online Access:https://www.mdpi.com/2075-5309/14/12/3866
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