Document Type
Article
Source of Publication
Energies
Publication Date
9-21-2024
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.
DOI Link
ISSN
Publisher
MDPI AG
Volume
17
Issue
18
Disciplines
Computer Sciences
Keywords
attention-based models, bidirectional fine tuning, deep learning, energy demand prediction, load forecasting, machine learning, power systems, time-series forecasting
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Kamalov, Firuz; Zicmane, Inga; Safaraliev, Murodbek; Smail, Linda; Senyuk, Mihail; and Matrenin, Pavel, "Attention-Based Load Forecasting with Bidirectional Finetuning" (2024). All Works. 6841.
https://zuscholars.zu.ac.ae/works/6841
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series