Attention-Based Load Forecasting with Bidirectional Finetuning

Document Type

Article

Source of Publication

Energies

Publication Date

9-1-2024

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.

ISSN

1996-1073

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

85204939276

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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