Residential building energy performance prediction at an urban scale using ensemble machine [....] скачать в хорошем качестве

Residential building energy performance prediction at an urban scale using ensemble machine [....]

Title: Residential building energy performance prediction at an urban scale using ensemble machine learning algorithms Authors: Ali, Usman; Bano, Sobia; Shamsi, Muhammad Haris; Sood, Divyanshu; Hoare, Cathal; O'Donnell, James Affiliation: School of Mechanical and Materials Engineering and UCD Energy Institute, UCD, Dublin, Ireland Keywords: building energy performance, urban building energy modeling, machine learning Session: Energy Modelling & Monitoring Paper Link: 10.35490/EC3.2023.200 Abstract: Data-driven performance assessment techniques have proven to be a viable solution at the urban scale. However, the data-driven performance assessments so far have often been limited in scope, scale and lacked key parameters. This paper proposes a workflow to integrate building archetypes' simulations, parametric analysis, and ensemble-based machine learning techniques to accurately predict building energy performance at an urban level. The result presented focuses on Irish residential buildings by generating a synthetic dataset using parametric analysis of crucial features of semi-detached building archetypes. The results show that the ensemble method gives higher-quality prediction when compared to traditional machine learning algorithms.