Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market

• URL: http://arxiv.org/abs/2502.04935v1
• Date: Fri, 07 Feb 2025 13:57:47 GMT
• Title: Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market
• Authors: Ciaran O'Connor, Mohamed Bahloul, Roberto Rossi, Steven Prestwich, Andrea Visentin,
• Abstract summary: integration of renewable energy into electricity markets poses significant challenges to price stability. This study explores the enhancement of probabilistic price prediction using Conformal Prediction (CP) techniques. We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques.
• Score: 0.0
• License:
• Abstract: The integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations. Accurate and reliable electricity price forecasting is crucial for effective market participation, where price dynamics can be significantly more challenging to predict. Probabilistic forecasting, through prediction intervals, efficiently quantifies the inherent uncertainties in electricity prices, supporting better decision-making for market participants. This study explores the enhancement of probabilistic price prediction using Conformal Prediction (CP) techniques, specifically Ensemble Batch Prediction Intervals and Sequential Predictive Conformal Inference. These methods provide precise and reliable prediction intervals, outperforming traditional models in validity metrics. We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques. This ensemble delivers both narrow prediction intervals and high coverage, leading to more reliable and accurate forecasts. We further evaluate the practical implications of CP techniques through a simulated trading algorithm applied to a battery storage system. The ensemble approach demonstrates improved financial returns in energy trading in both the Day-Ahead and Balancing Markets, highlighting its practical benefits for market participants.

Related papers

• Hybrid Forecasting of Geopolitical Events [71.73737011120103]
  SAGE is a hybrid forecasting system that combines human and machine generated forecasts. The system aggregates human and machine forecasts weighting both for propinquity and based on assessed skill. We show that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data.
  arXiv  Detail & Related papers  (2024-12-14T22:09:45Z)
• Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market [3.130428666578115]
  This study presents a Transformer-based deep learning model to forecast the price spread between real-time and day-ahead electricity prices in the ERCOT (Electric Reliability Council of Texas) market. The proposed model is trained under realistic constraints and validated using a walk-forward approach by updating the model every week. The results show that the strategy of trading only at the peak hour with a precision score of over 50% produces nearly consistent profit over the test period.
  arXiv  Detail & Related papers  (2024-11-25T20:04:16Z)
• On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices [41.94295877935867]
  We propose a novel approach to PEPF, extending the state of the art neural networks ensembles based methods through conformal inference based techniques. Experiments have been conducted on multiple market regions, achieving day-ahead forecasts with improved hourly coverage and stable probabilistic scores.
  arXiv  Detail & Related papers  (2024-04-03T13:22:47Z)
• Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Markets [0.0]
  conformal prediction (CP) is an emerging probabilistic forecasting method for day-ahead photovoltaic power predictions. Using CP in combination with certain bidding strategies can yield high profit with minimal energy imbalance. In concrete, using conformal predictive systems with k-nearest neighbors and Mondrian binning after random forest regression yields the best profit.
  arXiv  Detail & Related papers  (2024-03-29T12:34:57Z)
• Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding? [0.0]
  We study whether using CRPS learning, a novel weighting technique, leads to optimal decisions in day-ahead bidding. We find that increasing the diversity of an ensemble can have a positive impact on accuracy. The higher computational cost of using CRPS learning compared to an equal-weighted aggregation of distributions is not offset by higher profits.
  arXiv  Detail & Related papers  (2023-08-29T17:10:38Z)
• 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)
• Probabilistic forecasting of German electricity imbalance prices [0.0]
  The exponential growth of renewable energy capacity has brought much uncertainty to electricity prices and to electricity generation. For an energy trader participating in both markets, the forecasting of imbalance prices is of particular interest. The forecasting is performed 30 minutes before the delivery, so that the trader might still choose the trading place.
  arXiv  Detail & Related papers  (2022-05-23T16:32:20Z)
• 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)
• CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Motion Prediction [71.31516599226606]
  We present a new method to correctly predict the uncertainty associated with the predicted distribution of future trajectories. Our approach, CovariaceNet, is based on a Conditional Generative Model with Gaussian latent variables.
  arXiv  Detail & Related papers  (2021-09-07T09:38:24Z)
• Probabilistic electric load forecasting through Bayesian Mixture Density Networks [70.50488907591463]
  Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. We propose a novel PLF approach, framed on Bayesian Mixture Density Networks. To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated.
  arXiv  Detail & Related papers  (2020-12-23T16:21:34Z)
• 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.