Examining the Role of Passive Design Indicators in Energy Burden Reduction: Insights from a Machine Learning and Deep Learning Approach.

ABSTRACT: Passive design characteristics (PDC) play a pivotal role in reducing the energy burden on households without imposing additional financial constraints on project stakeholders. However, the scarcity of PDC data has posed a challenge in previous studies when assessing their energy-saving impact. To tackle this issue, this research introduces an innovative approach that combines deep learning-powered computer vision with machine learning techniques to examine the relationship between PDC and energy burden in residential buildings. In this study, we employ a convolutional neural network computer vision model to identify and measure key indicators, including window-to-wall ratio (WWR), external shading, and operable window types, using Google Street View images within the Chicago metropolitan a

SUBMITTER: Ghorbany S 

PROVIDER: S-EPMC11326486 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature