Document Type
Article
Publication Date
10-31-2021
Publication Source
European Journal of Advances in Engineering and Technology
Abstract
This paper presents a detailed investigation into various regression techniques applied to predict building energy consumption. The dataset utilized in this study encompasses diverse attributes related to buildings, including size, location, usage type, construction materials, and energy consumption patterns, alongside weather-related data such as temperature, humidity, and precipitation. After preprocessing, which includes loading, inspecting, and imputing missing data, the dataset undergoes evaluation using regression techniques including Ridge Regression, Bayesian Ridge, Linear Regression, Orthogonal Matching Pursuit, Elastic Net, Huber Regression, Lasso Regression, and Passive Aggressive Regression. Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2), Root Mean Squared Logarithmic Error (RMSLE), and Mean Absolute Percentage Error (MAPE) are employed to assess each model's performance. The results offer valuable insights into predictive accuracy, computational efficiency, and robustness, aiding in the selection of the most suitable regression technique for building energy prediction tasks.
ISBN/ISSN
2394-658X
Copyright
© 2021 Sharma V, 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
8
Issue
10
Keywords
Building energy prediction, Regression techniques, Ridge Regression, Bayesian Ridge, Linear Regression, Orthogonal Matching Pursuit, Elastic Net, Huber Regression, Lasso Regression, Passive Aggressive Regression, Data preprocessing, Model evaluation, Computational efficiency, Predictive accuracy, Robustness, Sustainability
eCommons Citation
Sharma, Vibhu, "A Comprehensive Exploration of Regression Techniques for Building Energy Prediction" (2021). Mechanical and Aerospace Engineering Graduate Student Publications. 15.
https://ecommons.udayton.edu/mee_grad_pub/15
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. DOI: https://doi.org/10.5281/zenodo.11103735