# Distributional neural networks for electricity price forecasting

arXiv: https://arxiv.org/abs/2207.02832 · HTML: https://mlsanity.com/paper/2207.02832/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 100 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 12 of 12)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark](https://mlsanity.com/paper/2008.08004/) (2020)
- [Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx](https://mlsanity.com/paper/2104.05522/) (2021)
- [Forecasting: theory and practice](https://mlsanity.com/paper/2012.03854/) (2020)
- [Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks](https://mlsanity.com/paper/1805.06649/) (2018)
- [CRPS Learning](https://mlsanity.com/paper/2102.00968/) (2021)
- [Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories](https://mlsanity.com/paper/2005.01365/) (2020)
- [Electricity Price Forecasting: The Dawn of Machine Learning](https://mlsanity.com/paper/2204.00883/) (2022)
- [Forecasting the Intra-Day Spread Densities of Electricity Prices](https://mlsanity.com/paper/2002.10566/) (2020)
- [Controlled abstention neural networks for identifying skillful predictions for classification problems](https://mlsanity.com/paper/2104.08281/) (2021)
- [Panel semiparametric quantile regression neural network for electricity consumption forecasting](https://mlsanity.com/paper/2103.00711/) (2021)

## Built on by (most cited first, 24 of 24)

- [Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression](https://mlsanity.com/paper/2404.02270/) (2024)
- [A feature selection method based on Shapley values robust to concept shift in regression](https://mlsanity.com/paper/2304.14774/) (2023)
- [On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices](https://mlsanity.com/paper/2404.02722/) (2024)
- [Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices](https://mlsanity.com/paper/2303.10019/) (2023)
- [Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices](https://mlsanity.com/paper/2403.05441/) (2024)
- [Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?](https://mlsanity.com/paper/2308.15443/) (2023)
- [Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market](https://mlsanity.com/paper/2502.04935/) (2025)
- [Leveraging Asynchronous Cross-border Market Data for Improved Day-Ahead Electricity Price Forecasting in European Markets](https://mlsanity.com/paper/2507.13250/) (2025)
- [Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows](https://mlsanity.com/paper/2311.14033/) (2023)
- [NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shape](https://mlsanity.com/paper/2411.13921/) (2024)
- [Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting](https://mlsanity.com/paper/2504.02518/) (2025)
- [ReModels: Quantile Regression Averaging models](https://mlsanity.com/paper/2405.11372/) (2024)
- [Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting](https://mlsanity.com/paper/2509.19417/) (2025)
- [Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting](https://mlsanity.com/paper/2604.24705/) (2026)
- [Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?](https://mlsanity.com/paper/2609.00089/) (2026)
- [RNN(p) for Power Consumption Forecasting](https://mlsanity.com/paper/2209.01378/) (2022)
- [Optimizing Quantile-based Trading Strategies in Electricity Arbitrage](https://mlsanity.com/paper/2406.13851/) (2024)
- [Risk-averse policies for natural gas futures trading using distributional reinforcement learning](https://mlsanity.com/paper/2501.04421/) (2025)
- [Forecasting Probability Distributions of Financial Returns with Deep Neural Networks](https://mlsanity.com/paper/2508.18921/) (2025)
- [From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting](https://mlsanity.com/paper/2509.14113/) (2025)
- [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)
- [Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting](https://mlsanity.com/paper/2511.11701/) (2025)
- [Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting](https://mlsanity.com/paper/2512.04690/) (2025)
- [Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Study](https://mlsanity.com/paper/2608.17091/) (2026)

## Abstract (arXiv)

We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks. The model structure is based on a deep neural network that contains a so-called probability layer. The network's output is a parametric distribution with 2 (normal) or 4 (Johnson's SU) parameters. In a forecasting study involving day-ahead electricity prices in the German market, our approach significantly outperforms state-of-the-art benchmarks, including LASSO-estimated regressions and deep neural networks combined with Quantile Regression Averaging. The obtained results not only emphasize the importance of higher moments when modeling volatile electricity prices, but also -- given that probabilistic forecasting is the essence of risk management -- provide important implications for managing portfolios in the power sector.