A Review of Recent Trends in Electricity Price Forecasting Using Deep Learning Techniques

Precise electricity price forecasts are gaining importance as the economy evolves. For years, researchers have attempted to generate such forecasts using artificial intelligence techniques. Recently, there has been a surge in the application of deep learning methods. This paper aims to identify the latest developments in this field, present the most significant solutions, and highlight existing research gaps. Numerous articles published since 2023 that employ deep learning neural networks for electricity price forecasting are analyzed. In addition to describing individual novel models, the paper provides a summary of error metrics for selected forecasting systems, indicating the markets covered by each study. One of the key conclusions drawn from this review is the limitation in the length of test sets, which in some cases were restricted to only a few days. The review also underscores the rationale for employing hybrid approaches that combine different deep neural network architectures and often incorporate data preprocessing.

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Publication Details

Journal
Energies
Published
2025-12-08
DOI
https://doi.org/10.3390/en18246422
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.00
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article

A Review of Recent Trends in Electricity Price Forecasting Using Deep Learning Techniques

Energies
Energy Load and Power Forecasting
article

A Review of Recent Trends in Electricity Price Forecasting Using Deep Learning Techniques

article en

Abstract

Precise electricity price forecasts are gaining importance as the economy evolves. For years, researchers have attempted to generate such forecasts using artificial intelligence techniques. Recently, there has been a surge in the application of deep learning methods. This paper aims to identify the latest developments in this field, present the most significant solutions, and highlight existing research gaps. Numerous articles published since 2023 that employ deep learning neural networks for electricity price forecasting are analyzed. In addition to describing individual novel models, the paper provides a summary of error metrics for selected forecasting systems, indicating the markets covered by each study. One of the key conclusions drawn from this review is the limitation in the length of test sets, which in some cases were restricted to only a few days. The review also underscores the rationale for employing hybrid approaches that combine different deep neural network architectures and often incorporate data preprocessing.

EnergiesVol. 18(24)
Lodz University of Technology (PL)
Openalex Percentile: Top 47%
Energy Load and Power Forecasting
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