Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach

Georgios Tsoumplekas, Christos Athanasiadis, Dimitrios I. Doukas (), Antonios Chrysopoulos and Pericles Mitkas
Additional contact information
Georgios Tsoumplekas: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Christos Athanasiadis: Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Dimitrios I. Doukas: NET2GRID BV, 54630 Thessaloniki, Greece
Antonios Chrysopoulos: NET2GRID BV, 54630 Thessaloniki, Greece
Pericles Mitkas: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Energies, 2025, vol. 18, issue 3, 1-23

Abstract: Despite the rapid expansion of smart grids and large volumes of data at the individual consumer level, there are still various cases where adequate data collection to train accurate load forecasting models is challenging or even impossible. This paper proposes adapting an established Model-Agnostic Meta-Learning algorithm for short-term load forecasting in the context of few-shot learning. Specifically, the proposed method can rapidly adapt and generalize within any unknown load time series of arbitrary length using only minimal training samples. In this context, the meta-learning model learns an optimal set of initial parameters for a base-level learner recurrent neural network. The proposed model is evaluated using a dataset of historical load consumption data from real-world consumers. Despite the examined load series’ short length, it produces accurate forecasts outperforming transfer learning and task-specific machine learning methods by 12.5 % . To enhance robustness and fairness during model evaluation, a novel metric, mean average log percentage error, is proposed that alleviates the bias introduced by the commonly used MAPE metric. Finally, a series of studies to evaluate the model’s robustness under different hyperparameters and time series lengths is also conducted, demonstrating that the proposed approach consistently outperforms all other models.

Keywords: few-shot learning; meta-learning; model agnostic meta-learning; short-term load forecasting; smart grid (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: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/3/742/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/3/742/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:3:p:742-:d:1584777

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().