Publicação
Gredeadry PT-energy demand forecast: AI data challenge field lab
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | |
| dc.contributor.advisor | Belo , Rodrigo | |
| dc.contributor.author | Colmenares, Luis Fernando Soares | |
| dc.date.accessioned | 2026-05-27T09:15:43Z | |
| dc.date.available | 2026-05-27T09:15:43Z | |
| dc.date.issued | 2026-01-09 | |
| dc.date.submitted | 2026-01-09 | |
| dc.description.abstract | My individual contribution focused on the short-term energy demand forecasting task, based on a regression modeling approach. The objective was to predict demand across two horizons: day-ahead and same-day-next-week. This included defining the modeling strategy, preparing the data splits, tuning hyperparameters, and evaluating model performance. Additionally, detailed teaching notes were developed to support students step by step while preserving independent problem-solving. This contribution combined technical implementation with pedagogical design to ensure both accurate forecasting and effective learning guidance.. | eng |
| dc.identifier.tid | 204242231 | |
| dc.identifier.uri | http://hdl.handle.net/10362/203457 | |
| dc.language.iso | eng | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Machine learning | |
| dc.subject | Forecasting | |
| dc.subject | Energy demand | |
| dc.title | Gredeadry PT-energy demand forecast: AI data challenge field lab | eng |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics |