Attention-Based Load Forecasting with Bidirectional Finetuning

Firuz Kamalov, Inga Zicmane (), Murodbek Safaraliev, Linda Smail, Mihail Senyuk and Pavel Matrenin
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Firuz Kamalov: Department of Electrical Engineering, Canadian University Dubai, Dubai 117781, United Arab Emirates
Inga Zicmane: Faculty of Electrical and Environmental Engineering, Riga Technical University, 1048 Riga, Latvia
Murodbek Safaraliev: Ural Power Engineering Institute, Ural Federal University, 620002 Yekaterinburg, Russia
Mihail Senyuk: Ural Power Engineering Institute, Ural Federal University, 620002 Yekaterinburg, Russia
Pavel Matrenin: Ural Power Engineering Institute, Ural Federal University, 620002 Yekaterinburg, Russia

Energies, 2024, vol. 17, issue 18, 1-16

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

Keywords: load forecasting; bidirectional fine tuning; attention-based models; time-series forecasting; power systems; energy demand prediction; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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