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

TL;DR

This paper reviews deep learning approaches for electricity price forecasting across various markets, highlighting recent trends, gaps, and proposing a unified evaluation framework.

Contribution

It introduces a unified taxonomy for deep learning models in EPF and analyzes recent trends and gaps in market-specific modeling strategies.

Findings

Shift toward probabilistic and market-aware deep learning models

Limited focus on intraday and balancing markets

Identifies need for market-specific modeling strategies

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.