mlsanityData to 14 Oct 2026

Distributional neural networks for electricity price forecasting

arXiv 2207.02832 ↗First posted 6 Jul 2022q-fin.ST stat.AP stat.MLcited by 24

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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.

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.

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12 of 100 references.

References · 12

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  2. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  3. 2020Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmarkcited by 108
  4. 2021Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSxcited by 64
  5. 2020Forecasting: theory and practicecited by 36
  6. 2018Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworkscited by 21
  7. 2021CRPS Learningcited by 21
  8. 2020Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectoriescited by 15
  9. 2022Electricity Price Forecasting: The Dawn of Machine Learningcited by 12
  10. 2020Forecasting the Intra-Day Spread Densities of Electricity Pricescited by 4
  11. 2021Controlled abstention neural networks for identifying skillful predictions for classification problemscited by 3
  12. 2021Panel semiparametric quantile regression neural network for electricity consumption forecastingcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 6; 2025: 10; 2026: 3.

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  1. 2024Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regressioncited by 7
  2. 2023A feature selection method based on Shapley values robust to concept shift in regressioncited by 6
  3. 2024On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity pricescited by 5
  4. 2023Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Pricescited by 4
  5. 2024Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Pricescited by 4
  6. 2023Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?cited by 3
  7. 2025Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Marketcited by 3
  8. 2025Leveraging Asynchronous Cross-border Market Data for Improved Day-Ahead Electricity Price Forecasting in European Marketscited by 3
  9. 2023Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flowscited by 2
  10. 2024NBMLSS: probabilistic forecasting of electricity prices via Neural Basis Models for Location Scale and Shapecited by 2
  11. 2025Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecastingcited by 2
  12. 2024ReModels: Quantile Regression Averaging modelscited by 1
12 more
  1. 2025Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecastingcited by 1
  2. 2026Energy-Arena: A Dynamic Benchmark for Operational Energy Forecastingcited by 1
  3. 2026Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?cited by 1
  4. 2022RNN(p) for Power Consumption Forecastingcited by 0
  5. 2024Optimizing Quantile-based Trading Strategies in Electricity Arbitragecited by 0
  6. 2025Risk-averse policies for natural gas futures trading using distributional reinforcement learningcited by 0
  7. 2025Forecasting Probability Distributions of Financial Returns with Deep Neural Networkscited by 0
  8. 2025From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecastingcited by 0
  9. 2025The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Marketscited by 0
  10. 2025Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecastingcited by 0
  11. 2025Recurrent Neural Networks with Linear Structures for Electricity Price Forecastingcited by 0
  12. 2026Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Studycited by 0