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Project # 26
An Efficient Model for Predicting Energy Consumption in a Data-Driven Industry Building Using Machine Learning
| Team Lead: | Abigail Ibarra |
| Other Team Members: | Ajmaeen Basher | Edwin Cardona | Cameron Chadick | Steven Jacinto Estala | Blaze Paul | Safwan Zaeem |
| Faculty Advisor(s): | Aera K. Leboulluec |
| Department: | IMSE |
| Project Type: | Other Undergraduate |
| Title: | An Efficient Model for Predicting Energy Consumption in a Data-Driven Industry Building Using Machine Learning |
| Abstract: | This research uses data collected from the Korean steel industry, including variables such as usage, lagging and leading reactive power, lagging and leading power factor, carbon dioxide emissions, and load types. The study aims to predict energy consumption using machine learning regression algorithms, including multiple linear regression, random forest, and k-nearest neighbors. The results indicate that the multiple linear regression model yields the best performance, with the lowest mean squared error and highest R-squared value. This model can be used to predict future energy consumption and optimize energy utilization. |
| Poster: | View Poster #26 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |