Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?

• URL: http://arxiv.org/abs/2308.15443v1
• Date: Tue, 29 Aug 2023 17:10:38 GMT
• Title: Combining predictive distributions of electricity prices: Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?
• Authors: Weronika Nitka and Rafa{\l} Weron
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no model is perfect and averaging generally improves forecasting performance. In this article we address the question of whether using CRPS learning, a novel weighting technique minimizing the continuous ranked probability score (CRPS), leads to optimal decisions in day-ahead bidding. To this end, we conduct an empirical study using hourly day-ahead electricity prices from the German EPEX market. We find that increasing the diversity of an ensemble can have a positive impact on accuracy. At the same time, the higher computational cost of using CRPS learning compared to an equal-weighted aggregation of distributions is not offset by higher profits, despite significantly more accurate predictions.

Related papers

• Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand [8.068451210598678]
  Power systems operate under uncertainty originating from multiple factors that are impossible to account for deterministically. Recent progress in deep learning has helped to significantly improve the accuracy of point forecasts. We propose a novel general approach for distributional forecasting capable of predicting arbitrary quantiles.
  arXiv  Detail & Related papers  (2024-04-26T14:43:19Z)
• Postprocessing of point predictions for probabilistic forecasting of electricity prices: Diversity matters [0.0]
  We examine three postprocessing methods for converting point forecasts into probabilistic ones. We find that combining its predictive distributions with those of the other two methods results in an improvement of ca. 7.5%. Remarkably, the performance of this combination is at par with state-of-the-art Distributional Deep Neural Networks.
  arXiv  Detail & Related papers  (2024-04-02T19:50:36Z)
• 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)
• Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices [0.0]
  We present a first study of Bayesian forecasting of electricity prices traded on the German continuous intraday market. For validation we use the exceedingly volatile electricity prices of 2022, which have hardly been the subject of forecasting studies before. We challenge the declared gold standard of using LASSO for feature selection in electricity price forecasting by presenting strong statistical evidence that Orthogonal Matching Pursuit (OMP) leads to better forecasting performance.
  arXiv  Detail & Related papers  (2024-03-08T16:51:27Z)
• 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 a training-free extreme value enhancement strategy named ExEnsemble, which increases the variance of pixel values and improves the forecast robustness. 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)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• 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)
• Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach [4.992622806418143]
  Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. This paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model.
  arXiv  Detail & Related papers  (2023-04-30T00:43:26Z)
• Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows [62.997667081978825]
  In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the EPEX spot markets in a distinct hourly pattern. This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts.
  arXiv  Detail & Related papers  (2022-05-27T08:38:20Z)
• Taming Overconfident Prediction on Unlabeled Data from Hindsight [50.9088560433925]
  Minimizing prediction uncertainty on unlabeled data is a key factor to achieve good performance in semi-supervised learning. This paper proposes a dual mechanism, named ADaptive Sharpening (ADS), which first applies a soft-threshold to adaptively mask out determinate and negligible predictions. ADS significantly improves the state-of-the-art SSL methods by making it a plug-in.
  arXiv  Detail & Related papers  (2021-12-15T15:17:02Z)
• Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration [107.15813002403905]
  Decision makers often need to rely on imperfect probabilistic forecasts. We propose a compensation mechanism ensuring that the forecasted utility matches the actually accrued utility. We demonstrate an application showing how passengers could confidently optimize individual travel plans based on flight delay probabilities.
  arXiv  Detail & Related papers  (2020-11-15T08:22:39Z)

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.