Advanced Forecasting Techniques for Smart Grids to Enhance Energy Efficiency and Sustainability
Data-driven forecasting for smart grid decision support, focusing on accuracy, robustness, and deployment realism.
Details
Abstract: This study evaluates advanced energy consumption forecasting models as a foundation for proactive and intelligent energy management in smart grids. Using real-world weather and consumption data from a student residence in Bologna, Italy, LSTM, Transformer, and benchmark models were compared. Results show that LSTM achieved the highest accuracy, while Transformer models demonstrated strong potential for capturing long-term dependencies.
Publisher: ACM
Year: 2024