Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables

Amir Shahcheraghian () and Adrian Ilinca ()
Additional contact information
Amir Shahcheraghian: Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
Adrian Ilinca: Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada

Energies, 2024, vol. 17, issue 18, 1-22

Abstract: Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver’s temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact.

Keywords: energy consumption; machine learning; meteorological data; regression models; energy efficiency; UBC Campus; neural networks; electricity usage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/18/4714/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/18/4714/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:18:p:4714-:d:1482914

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().