Energies, Journal Year: 2024, Volume and Issue: 17(18), P. 4699 - 4699

Published: Sept. 21, 2024

Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances accuracy by fine tuning an attention-based model with bidirectional reading time-series data. By incorporating both forward backward temporal dependencies, gains more comprehensive understanding consumption patterns, leading improved performance. We present mathematical framework supporting this approach, demonstrating its potential reduce errors improve robustness. Experimental results on real-world datasets indicate our outperforms state-of-the-art conventional models, providing tool short medium-term forecasting. research highlights importance context in practical implications grid stability, economic efficiency, resource planning.

Language: Английский