# Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs

arXiv: https://arxiv.org/abs/2310.03339 · HTML: https://mlsanity.com/paper/2310.03339/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [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)
- [Electricity Price Forecasting: The Dawn of Machine Learning](https://mlsanity.com/paper/2204.00883/) (2022)
- [Understanding electricity prices beyond the merit order principle using explainable AI](https://mlsanity.com/paper/2212.04805/) (2022)
- [Validation Methods for Energy Time Series Scenarios from Deep Generative Models](https://mlsanity.com/paper/2110.14451/) (2021)
- [Initial analysis of the impact of the Ukrainian power grid synchronization with Continental Europe](https://mlsanity.com/paper/2204.07508/) (2022)

## Built on by (most cited first, 4 of 4)

- [Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market](https://mlsanity.com/paper/2502.04935/) (2025)
- [Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows](https://mlsanity.com/paper/2311.14033/) (2023)
- [Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market](https://mlsanity.com/paper/2412.00062/) (2024)
- [Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting](https://mlsanity.com/paper/2604.14739/) (2026)

## Abstract (arXiv)

Accurate forecasts of electricity prices are crucial for the management of electric power systems and the development of smart applications. European electricity prices have risen substantially and became highly volatile after the Russian invasion of Ukraine, challenging established forecasting methods. Here, we present a Long Short-Term Memory (LSTM) model for the German-Luxembourg day-ahead electricity prices addressing these challenges. The recurrent structure of the LSTM allows the model to adapt to trends, while the joint prediction of both mean and standard deviation enables a probabilistic prediction. Using a physics-inspired approach - superstatistics - to derive an explanation for the statistics of prices, we show that the LSTM model faithfully reproduces both prices and their volatility.