Document Type
Article
Publication Date
1-1-2022
Publication Source
Journal of Technological Innovations
Abstract
This study investigates the application of machine learning techniques to predict energy consumption in buildings. Five machine learning models (linear regression, decision trees, random forest, support vector machines, and neural networks) are evaluated, and a permutation feature importance analysis is conducted to identify the most influential features. The random forest model emerges as the top performer, achieving a mean absolute error of 15.2%. Temperature, solar radiation, and relative humidity are found to be the important features in energy prediction. The study demonstrates the potential of machine learning for energy prediction in buildings, contributing to more efficient energy management and sustainability.
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.
Publisher
JTI Publications
Volume
3
Issue
1
Keywords
Machine Learning, Energy Consumption Prediction, Building Energy Management, Energy Efficiency, Predictive Analytics Sustainable Building Practices, Energy Demand Forecasting, Data-Driven Optimization, Energy Conservation Policy
eCommons Citation
Sharma, Vibhu, "Exploring the Predictive Power of Machine Learning for Energy Consumption in Buildings" (2022). Mechanical and Aerospace Engineering Graduate Student Publications. 19.
https://ecommons.udayton.edu/mee_grad_pub/19
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.