Parametric and Generative Forecasts of Day-Ahead Market Curves for Storage Optimization

• URL: http://arxiv.org/abs/2601.20226v1
• Date: Wed, 28 Jan 2026 03:56:05 GMT
• Title: Parametric and Generative Forecasts of Day-Ahead Market Curves for Storage Optimization
• Authors: Julian Gutierrez, Redouane Silvente,
• Abstract summary: We present two machine learning frameworks for forecasting aggregated curves and optimizing storage in the EPEX SPOT day-ahead market.<n>First, a fast parametric model forecasts hourly demand and supply curves in a low-dimensional and grid-bust representation.<n>Second, for a more comprehensive analysis, we employ generative models that learn the joint distribution of 24-hour order-level submissions.
• Score: 1.4180331276028662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present two machine learning frameworks for forecasting aggregated curves and optimizing storage in the EPEX SPOT day-ahead market. First, a fast parametric model forecasts hourly demand and supply curves in a low-dimensional and grid-robust representation, with minimum and maximum volumes combined with a Chebyshev polynomial for the elastic segment. The model enables daily use with low error and clear interpretability. Second, for a more comprehensive analysis, though less suited to daily operation, we employ generative models that learn the joint distribution of 24-hour order-level submissions given weather and fuel variables. These models generate synthetic daily scenarios of individual buy and sell orders, which, once aggregated, yield hourly supply and demand curves. Based on these forecasts, we optimize a price-making storage strategy, quantify revenue distributions, and highlight the price-compression effect with lower peaks, higher off-peak levels, and diminishing returns as capacity expands.

Related papers

• TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting [51.37167759339485]
  We propose Temporal-Aligned Transformer (TAT), a multi-horizon forecaster leveraging apriori-known context variables for improving predictive performance.<n>Our model consists of an encoder and decoder, both embedded with a novel Temporal Alignment Attention (TAA) designed to learn context-dependent alignment for peak demand forecasting.<n>We demonstrate that TAT brings up to 30% accuracy on peak demand forecasting while maintaining competitive overall performance compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-14T14:51:24Z)
• $K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting [11.83736650205371]
  Probabilistic Time Series Forecasting (PTSF) plays a crucial role in decision-making across various fields, including economics, energy, and transportation.<n>We introduce $K2$VAE, an efficient VAE-based generative model that transforms nonlinear time series into a linear dynamical system.<n>$K2$VAE outperforms state-of-the-art methods in both short- and long-term PTSF, providing a more efficient and accurate solution.
  arXiv  Detail & Related papers  (2025-05-29T02:52:59Z)
• Decoding Futures Price Dynamics: A Regularized Sparse Autoencoder for Interpretable Multi-Horizon Forecasting and Factor Discovery [0.32634122554914]
  This paper presents a Regularized Sparse Autoencoder (RSAE) for simultaneous multi-horizon commodity price prediction.<n>Our findings indicate the RSAE offers competitive multi-horizon forecasting accuracy and data-driven insights into price dynamics.
  arXiv  Detail & Related papers  (2025-05-11T00:21:53Z)
• Sundial: A Family of Highly Capable Time Series Foundation Models [47.27032162475962]
  We introduce Sundial, a family of native, flexible, and scalable time series foundation models.<n>Our models are pre-trained without specifying any prior distribution and can generate multiple probable predictions.<n>Sundial achieves state-of-the-art results on both point and probabilistic forecasting benchmarks with a just-in-time inference speed.
  arXiv  Detail & Related papers  (2025-02-02T14:52:50Z)
• Modelling financial volume curves with hierarchical Poisson processes [0.8624680612413765]
  A common strategy is to trade a desired quantity across many orders in line with the expected volume curve throughout the day. We introduce a hierarchical Poisson process model for the intensity functions of admixtures of inhomogenous Poisson processes, which represent the trading times of the stock throughout the day. We demonstrate the method on datasets from the Trade and Quote repository maintained by Wharton Research Data Services.
  arXiv  Detail & Related papers  (2024-06-01T12:03:57Z)
• 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)
• 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)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [53.37679435230207]
  We propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data.
  arXiv  Detail & Related papers  (2022-09-23T16:13:47Z)
• 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)
• Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting [93.73198973454944]
  Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.
  arXiv  Detail & Related papers  (2021-07-05T10:15:23Z)
• Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach [29.11201102550876]
  We build a semi-parametric structural model to learn individual price elasticity and predict counterfactual demand. We propose a multi-period dynamic pricing algorithm to maximize the overall profit of a perishable product over its finite selling horizon. The proposed framework has been successfully deployed to the well-known e-commerce fresh retail scenario - Freshippo.
  arXiv  Detail & Related papers  (2021-05-18T07:01:37Z)
• Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network [71.25144476293507]
  We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market. With representation learning, we derived an embedding called Stock2Vec, which gives us insight for the relationship among different stocks. Our hybrid framework integrates both advantages and achieves better performance on the stock price prediction task than several popular benchmarked models.
  arXiv  Detail & Related papers  (2020-09-29T22:54:30Z)
• 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.