ItemConference Paper

Prediction of Campus Energy Consumption Patterns Using Machine Learning Techniques

Title
Prediction of Campus Energy Consumption Patterns Using Machine Learning Techniques
Creator
Nwibo, Ezekiel Gabriel; Iwendi, Celestine; Sharma, Vandana; Nwigwe, Simon; Ojo, Olayinka Anthony; Odirichukwu, Jacinta Chioma
Description
The exponential increase in campus energy consumption results from the rise in population density, leading to urbanisation and the use of higher energy-intensive devices within the environment. This study explored high-performance data analytics techniques to visualise energy consumption across buildings using datasets obtained from a load audit of the entire distribution network within the Federal University of Technology, Owerri (FUTO). Advanced time series models were used to predict and forecast the consumption patterns for a year. Visualisations for this research provided detailed insights into the energy profile across all the clusters, while the SARIMA, ARIMA, and Prophet models predicted the energy demands. The heatmap for the correlation matrix reveals a constant energy scale throughout the week (weekend average energy usage is at least 40% of the weekday). A comparative performance was done to analyse the scalability and predictive abilities of the individual models. Results from the study indicate that SARIMA has the lowest mean square error (4.4896) and the highest R2 score (0.8362). The study concludes that the adoption of machine learning models for energy forecasting and prediction is vital for modern-day energy management in the University. 2025 IEEE.
Source
2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
ARIMA; Artificial Intelligence; Energy Management; Machine Learning; Prophet; Renewable Energy; SARIMA
Coverage
Nwibo E.G., School of Arts and Creative Technologies, University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Iwendi C., School of Arts and Creative Technologies, University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Nwigwe S., Doctoral College, University of Greater Manchester, Department of Research, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Odirichukwu J.C., Federal University of Technology Owerri, Department of Computer Science, Nigeria
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155421-7;
Format
online
Language
English
Type
Conference paper

Citation

Nwibo, Ezekiel Gabriel; Iwendi, Celestine; Sharma, Vandana; Nwigwe, Simon; Ojo, Olayinka Anthony; Odirichukwu, Jacinta Chioma, “Prediction of Campus Energy Consumption Patterns Using Machine Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26098.

Output Formats