2025
doi: 10.12720/jait.16.5.623-631
Unveiling the Potential of Transformer-Based Models for Efficient Time-Series Energy Forecasting
Imane Moustati*
,
Noreddine Gherabi
- National School of Applied Sciences, Sultan Moulay Slimane University, Khouribga, Morocco
- Manuscript receivedDecember 26, 2024
- revisedJanuary 23, 2025
- acceptedFebruary 11, 2025
- publishedMay 9, 2025
Abstract
Accurately forecasting energy consumption is critical in optimizing energy management, reducing costs, and enhancing grid stability. This study uses smart meter data to evaluate the performance of four transformer-based models—Vanilla Transformer, Autoformer, Informer, and SpaceTimeFormer—for energy consumption forecasting. The models are evaluated against statistical benchmarks, with results indicating that Autoformer is the most efficient transformer, achieving the best balance between accuracy and computational complexity, with a Mean Absolute Error (MAE) of 0.540, a Root Mean Square Error (RMSE) of 0.764, a Mean Absolute Percentage Error (MAPE) of 0.091, and an R² of 0.979. The study focuses on transformer models, establishing their utility for time-series forecasting and identifying Autoformer as the most suitable for this dataset. These findings highlight the transformative potential of advanced architectures for handling complex temporal data and provide a benchmark for future research in energy consumption forecasting.
Keywords
- transformers models
- Autoformer
- time-series forecasting
- energy consumption prediction
- smart meters
How to Cite
Imane Moustati and Noreddine Gherabi, "Unveiling the Potential of Transformer-Based Models for Efficient Time-Series Energy Forecasting," Journal of Advances in Information Technology, vol. 16, no. 5, pp. 623-631, 2025. https://doi.org/10.12720/jait.16.5.623-631
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
