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arxiv: 2602.10071 · v2 · submitted 2026-02-10 · 💱 q-fin.CP

Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets

Runyao Yu , Derek W. Bunn , Julia Lin , Jochen Stiasny , Fabian Leimgruber , Tara Esterl , Yuchen Tao , Lianlian Qi
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Yujie Chen Wentao Wang Jochen L. Cremer
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Pith reviewed 2026-05-16 03:04 UTC · model grok-4.3

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keywords electricity price forecastingdeep learningmodel taxonomyday-ahead marketintraday marketbalancing marketprobabilistic forecasting
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The pith

A unified taxonomy decomposes deep learning models into backbone, head, and loss for comparing electricity price forecasts across markets.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This review establishes a consistent framework for analyzing deep learning methods applied to electricity price forecasting in day-ahead, intraday, and balancing markets. It does so by breaking models into backbone, head, and loss components to enable clearer cross-study comparisons of architectures and objectives. A sympathetic reader would care because better forecasts directly support power system operations and market trading decisions. The analysis identifies a shift toward probabilistic and market-aware designs while noting insufficient work on intraday and balancing segments.

Core claim

The paper claims that existing deep learning studies for electricity price forecasting can be decomposed into backbone, head, and loss components to provide a consistent evaluation perspective across day-ahead, intraday, and balancing markets, which reveals trends toward probabilistic, microstructure-centric, and market-aware designs along with key gaps in coverage of intraday and balancing markets.

What carries the argument

The unified taxonomy that decomposes deep learning models into backbone, head, and loss components to support consistent comparisons.

If this is right

  • Consistent cross-study comparisons of model designs become feasible using the shared taxonomy.
  • Trends emerge showing greater adoption of probabilistic outputs and microstructure-aware components.
  • Research gaps are clarified, particularly the under-representation of intraday and balancing markets.
  • Market-specific modeling strategies are identified as necessary for advancing the field.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The taxonomy could be tested for reuse in other volatile time-series domains such as energy demand or commodity prices.
  • Model builders might systematically swap heads or losses to adapt forecasts to new market rules.
  • Greater coverage of intraday and balancing data could improve real-time trading accuracy beyond what day-ahead models achieve.
  • The framework may benefit from explicit inclusion of preprocessing choices to handle price spikes and missing data.

Load-bearing premise

Existing deep learning studies in electricity price forecasting can be meaningfully decomposed and compared using only the backbone-head-loss framework without overlooking critical market-specific or data-related differences.

What would settle it

A new review or empirical study showing that many published deep learning models for electricity price forecasting cannot be classified under the backbone-head-loss taxonomy or that this decomposition misses important performance drivers tied to market data differences would undermine the taxonomy's utility.

Figures

Figures reproduced from arXiv: 2602.10071 by Derek W. Bunn, Fabian Leimgruber, Jochen L. Cremer, Jochen Stiasny, Julia Lin, Lianlian Qi, Runyao Yu, Tara Esterl, Wentao Wang, Yuchen Tao, Yujie Chen.

Figure 1
Figure 1. Figure 1: Visualization of day-ahead, intraday, and imbalance prices from 1-15 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Backbone comparison of deep learning models. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
read the original abstract

Electricity price forecasting (EPF) plays a critical role in power system operation and market decision making. While existing review studies have provided valuable insights into forecasting horizons, market mechanisms, and evaluation practices, the rapid adoption of deep learning has introduced increasingly diverse model architectures, output structures, and training objectives that remain insufficiently analyzed in depth. This paper presents a structured review of deep learning methods for EPF in day-ahead, intraday, and balancing markets. Specifically, We introduce a unified taxonomy that decomposes deep learning models into backbone, head, and loss components, providing a consistent evaluation perspective across studies. Using this framework, we analyze recent trends in deep learning components across markets. Our study highlights the shift toward probabilistic, microstructure-centric, and market-aware designs. We further identify key gaps in the literature, including limited attention to intraday and balancing markets and the need for market-specific modeling strategies, thereby helping to consolidate and advance existing review studies.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is a literature review and introduces no new free parameters, axioms, or invented entities; it relies on standard review practices and existing published studies.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting

    q-fin.CP 2026-05 unverdicted novelty 5.0

    A market-rule-informed neural network for imbalance electricity price forecasting matches generic deep learning accuracy while using substantially fewer parameters and less training time.

Reference graph

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