Transformer Based Architecture for Smart Grid Energy Consumption Forecasting
Hadish, Siem Guizani, Maher Aloqaily, Moayad Khan, Latif U.
Hadish, Siem
Guizani, Maher
Aloqaily, Moayad
Khan, Latif U.
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Energy consumption forecasting in microgrids is critical to ensure efficiency, reliability, and sustainability. It can be beneficial for optimizing grids, reducing costs, demand-supply balancing, and enhancing sustainability. The forecast scope can range from hours or days to months and years, based on the goal of the entity doing the prediction. In the case of microgrids, the focus tends to be in the short to medium range, hours to days to achieve efficient energy distribution, cost minimization, and renewable energy utilization. In this dynamic and localized setup, the energy consumption forecast is an integral part of the process. Therefore, in this paper, we explore different ways we can generate reliable forecasts of energy consumption by a group of residential houses from the Pecan Street dataset. Leveraging on the advancement of LLMs and transformers, we propose an LLM-based model and we benchmark our findings against other models. Our results highlight the advantage and performance that can be achieved with our transformer based which demonstrates a superior predictive accuracy over other architectures.
Citation
S. Hadish, M. Guizani, M. Aloqaily and L. U. Khan, "Transformer Based Architecture for Smart Grid Energy Consumption Forecasting," 2025 International Wireless Communications and Mobile Computing (IWCMC), Abu Dhabi, United Arab Emirates, 2025, pp. 1726-1731, doi: 10.1109/IWCMC65282.2025.11059615
Source
21st International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Conference
21st IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Keywords
Energy consumption forecasting, microgrid, smart grid, Transformers
Subjects
Source
21st IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Publisher
IEEE