Document Type
Article
Publication Date
4-1-2022
Publication Source
International Journal of Science and Research
Abstract
Residential buildings account for a significant portion of global energy consumption, emphasizing the pressing need to optimize their energy efficiency. This paper proposes a novel approach utilizing advanced machine learning algorithms to accurately predict energy performance in residential buildings. Leveraging a dataset sourced from the UCI Machine Learning Repository, comprising diverse building shapes simulated in Ecotect with varying glazing areas, distributions, and orientations, our study aims to forecast critical real-valued responses pertaining to energy efficiency. Through meticulous preprocessing and feature engineering, coupled with state-of-the-art machine learning techniques such as Autogluon, PyCaret FLAML, and AutoSKLearn, our research demonstrates promising results, highlighting the transformative potential of machine learning in informing sustainable architectural practices.
ISBN/ISSN
2319-7064
Copyright
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Volume
11
Issue
4
Keywords
Energy efficiency analysis, Residential buildings, Machine learning techniques, Feature engineering, Predictive modeling, Sustainable architecture, Data-driven approach, Advanced algorithms, Preprocessing, Model evaluation
eCommons Citation
Sharma, Vibhu, "Energy Efficiency Analysis in Residential Buildings using Machine Learning Techniques" (2022). Mechanical and Aerospace Engineering Graduate Student Publications. 17.
https://ecommons.udayton.edu/mee_grad_pub/17
COinS
Comments
The document available for download is the published version, provided in compliance with the publisher's open-access policy. Permission documentation is on file.