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

• URL: http://arxiv.org/abs/2310.03339v1
• Date: Thu, 5 Oct 2023 06:47:28 GMT
• Title: Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs
• Authors: Julius Trebbien, Sebastian P\"utz, Benjamin Sch\"afer, Heidi S. Nyg{\aa}rd, Leonardo Rydin Gorj\~ao, Dirk Witthaut
• Abstract summary: We present a Long Short-Term Memory (LSTM) model for the German-Luxembourg day-ahead electricity prices. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Interval Forecasts for Gas Prices in the Face of Structural Breaks -- Statistical Models vs. Neural Networks [0.0]
  We investigate whether modern machine learning methods such as neural networks are more resilient against such changes. We see that, during the shock period, most models underestimate the variance while overestimating the variance in the after-shock period. Interestingly, the widely-used long-short term neural network is outperformed by its competitors.
  arXiv  Detail & Related papers  (2024-07-23T11:34:13Z)
• Revisiting Day-ahead Electricity Price: Simple Model Save Millions [7.088576782842557]
  We propose a simple piecewise linear model that significantly enhances forecast accuracy by directly deriving prices from readily forecastable demand-supply values. Experiments in the day-ahead electricity markets of Shanxi province and ISO New England reveal that such forecasts could potentially save residents millions of dollars a year.
  arXiv  Detail & Related papers  (2024-05-20T08:27:14Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine [0.0]
  The proposed methodology was constructed using hourly data from Ukraine's electricity consumption ranging from 2013 to 2020. Our hybrid model is very effective at forecasting long-term electricity consumption on an hourly resolution.
  arXiv  Detail & Related papers  (2023-04-11T12:15:50Z)
• A Hybrid Model for Forecasting Short-Term Electricity Demand [59.372588316558826]
  Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and LSTM encoder-decoders.
  arXiv  Detail & Related papers  (2022-05-20T22:13:25Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model [0.0]
  This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices. Case studies reveal that the proposed ILRCN model is accurate and efficient in electricity price forecasting.
  arXiv  Detail & Related papers  (2021-12-23T06:45:12Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories [0.0]
  Recent studies have shown that the hourly German Intraday Continuous Market is weak-form efficient. A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window. The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 hours of trading, but the approach allows for application to other continuous markets, especially in Europe.
  arXiv  Detail & Related papers  (2020-05-04T10:21:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.