Powering Electricity Forecasting with Transfer Learning

Kamalov, Firuz; Sulieman, Hana; Moussa, Sherif; Avante Reyes, Jorge; Safaraliev, Murodbek

doi:10.3390/en17030626

Open AccessArticle

Powering Electricity Forecasting with Transfer Learning

by

Firuz Kamalov

^1,*,

Hana Sulieman

^2,*

,

Sherif Moussa

¹,

Jorge Avante Reyes

¹ and

Murodbek Safaraliev

³

¹

Department of Electrical Engineering, Canadian University Dubai, Dubai 117781, United Arab Emirates

²

Department of Mathematics and Statistics, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates

³

Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia

^*

Authors to whom correspondence should be addressed.

Energies 2024, 17(3), 626; https://doi.org/10.3390/en17030626

Submission received: 6 December 2023 / Revised: 10 January 2024 / Accepted: 17 January 2024 / Published: 28 January 2024

(This article belongs to the Section A: Sustainable Energy)

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Abstract

Accurate forecasting is one of the keys to the efficient use of the limited existing energy resources and plays an important role in sustainable development. While most of the current research has focused on energy price forecasting, very few studies have considered medium-term (monthly) electricity generation. This research aims to fill this gap by proposing a novel forecasting approach based on zero-shot transfer learning. Specifically, we train a Neural Basis Expansion Analysis for Time Series (NBEATS) model on a vast dataset comprising diverse time series data. Then, the trained model is applied to forecast electric power generation using zero-shot learning. The results show that the proposed method achieves a lower error than the benchmark deep learning and statistical methods, especially in backtesting. Furthermore, the proposed method provides vastly superior execution time as it does not require problem-specific training.

Keywords: electricity forecasting; transfer learning; electricity generation; NBEATS; deep learning; medium-term

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MDPI and ACS Style

Kamalov, F.; Sulieman, H.; Moussa, S.; Avante Reyes, J.; Safaraliev, M. Powering Electricity Forecasting with Transfer Learning. Energies 2024, 17, 626. https://doi.org/10.3390/en17030626

AMA Style

Kamalov F, Sulieman H, Moussa S, Avante Reyes J, Safaraliev M. Powering Electricity Forecasting with Transfer Learning. Energies. 2024; 17(3):626. https://doi.org/10.3390/en17030626

Chicago/Turabian Style

Kamalov, Firuz, Hana Sulieman, Sherif Moussa, Jorge Avante Reyes, and Murodbek Safaraliev. 2024. "Powering Electricity Forecasting with Transfer Learning" Energies 17, no. 3: 626. https://doi.org/10.3390/en17030626

APA Style

Kamalov, F., Sulieman, H., Moussa, S., Avante Reyes, J., & Safaraliev, M. (2024). Powering Electricity Forecasting with Transfer Learning. Energies, 17(3), 626. https://doi.org/10.3390/en17030626

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Powering Electricity Forecasting with Transfer Learning

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