Work

Attention-Based Load Forecasting with Bidirectional Finetuning

Year: 2024

Type: article

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.

Source: Energies

DOI: 10.3390/en17184699

Authors: Firuz Kamalov, Inga Zicmane, Murodbek Safaraliev, Linda Smail, Mihail Senyuk, Pavel Matrenin

Institutions: Canadian University of Dubai, Riga Technical University, Ural Federal University, Zayed University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $2,955