# Electricity Price Forecasting: The Dawn of Machine Learning

arXiv: https://arxiv.org/abs/2204.00883 · HTML: https://mlsanity.com/paper/2204.00883/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2022.

## Built on by (most cited first, 12 of 12)

- [Distributional neural networks for electricity price forecasting](https://mlsanity.com/paper/2207.02832/) (2022)
- [Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows](https://mlsanity.com/paper/2205.13826/) (2022)
- [On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices](https://mlsanity.com/paper/2404.02722/) (2024)
- [Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs](https://mlsanity.com/paper/2310.03339/) (2023)
- [Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows](https://mlsanity.com/paper/2311.14033/) (2023)
- [Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods](https://mlsanity.com/paper/2411.03372/) (2024)
- [Explaining deep neural network models for electricity price forecasting with XAI](https://mlsanity.com/paper/2506.19894/) (2025)
- [Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning](https://mlsanity.com/paper/2601.02856/) (2026)
- [Forecasting Electricity Prices](https://mlsanity.com/paper/2204.11735/) (2022)
- [Forecasting Day-Ahead Electricity Prices in the Integrated Single Electricity Market: Addressing Volatility with Comparative Machine Learning Methods](https://mlsanity.com/paper/2408.05628/) (2024)
- [The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets](https://mlsanity.com/paper/2511.05523/) (2025)
- [Electricity price forecasting across Norway's five bidding zones in the post-crisis era](https://mlsanity.com/paper/2604.26634/) (2026)

## Abstract (arXiv)

Electricity price forecasting (EPF) is a branch of forecasting on the interface of electrical engineering, statistics, computer science, and finance, which focuses on predicting prices in wholesale electricity markets for a whole spectrum of horizons. These range from a few minutes (real-time/intraday auctions and continuous trading), through days (day-ahead auctions), to weeks, months or even years (exchange and over-the-counter traded futures and forward contracts). Over the last 25 years, various methods and computational tools have been applied to intraday and day-ahead EPF. Until the early 2010s, the field was dominated by relatively small linear regression models and (artificial) neural networks, typically with no more than two dozen inputs. As time passed, more data and more computational power became available. The models grew larger to the extent where expert knowledge was no longer enough to manage the complex structures. This, in turn, led to the introduction of machine learning (ML) techniques in this rapidly developing and fascinating area. Here, we provide an overview of the main trends and EPF models as of 2022.