Cover Image

Probabilistic intraday electricity price forecasting using generative machine learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Jieyu, Lerch, Sebastian, Schienle, Melanie, Serafin, Tomasz, Weron, Rafał
Format:	Preprint
Published:	2025
Subjects:	Applications Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression
by: Lipiecki, Arkadiusz, et al.
Published: (2024)

An adaptive standardisation methodology for Day-Ahead electricity price forecasting
by: Sebastián, Carlos, et al.
Published: (2023)

Data-driven Calibration Sample Selection and Forecast Combination in Electricity Price Forecasting: An Application of the ARHNN Method
by: Serafin, Tomasz, et al.
Published: (2025)

Forecasting Electricity Prices
by: Maciejowska, Katarzyna, et al.
Published: (2022)

Probabilistic forecasting with a hybrid Factor-QRA approach: Application to electricity trading
by: Maciejowska, Katarzyna, et al.
Published: (2023)

Scenario generation of intraday electricity price paths for optimal trading in continuous markets
by: Puć, Andrzej, et al.
Published: (2026)

A two-step machine learning approach to statistical post-processing of weather forecasts for power generation
by: Baran, Ágnes, et al.
Published: (2022)

Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts
by: Lipiecki, Arkadiusz, et al.
Published: (2025)

Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning
by: Horat, Nina, et al.
Published: (2024)

Corrected Support Vector Regression for intraday point forecasting of prices in the continuous power market
by: Puć, Andrzej, et al.
Published: (2024)

Model Determination for High-Dimensional Longitudinal Data with Missing Observations: An Application to Microfinance Data
by: Rüter, Lotta, et al.
Published: (2024)

Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods
by: Chen, Jieyu, et al.
Published: (2025)

Probabilistic measures afford fair comparisons of AIWP and NWP model output
by: Gneiting, Tilmann, et al.
Published: (2025)

Do we actually understand the impact of renewables on electricity prices? A causal inference approach
by: Cacciarelli, Davide, et al.
Published: (2025)

A practical scenario generation method for electricity prices on day-ahead and intraday spot markets
by: Papadimitriou, Chrysanthi, et al.
Published: (2024)

Machine learning-based probabilistic forecasting of solar irradiance in Chile
by: Baran, Sándor, et al.
Published: (2024)

Analysis of ELSA COVID-19 Substudy response rate using machine learning algorithms
by: Qazvini, Marjan
Published: (2024)

Classification problem in liability insurance using machine learning models: a comparative study
by: Qazvini, Marjan
Published: (2024)

Error-controlled non-additive interaction discovery in machine learning models
by: Chen, Winston, et al.
Published: (2024)

Causal machine learning for predicting treatment outcomes
by: Feuerriegel, Stefan, et al.
Published: (2024)

Generative machine learning methods for multivariate ensemble post-processing
by: Chen, Jieyu, et al.
Published: (2022)

Regularization for electricity price forecasting
by: Uniejewski, Bartosz
Published: (2024)

A generative machine learning model for designing metal hydrides applied to hydrogen storage
by: Liu, Xiyuan, et al.
Published: (2026)

Automobile demand forecasting: Spatiotemporal and hierarchical modeling, life cycle dynamics, and user-generated online information
by: Nahrendorf, Tom, et al.
Published: (2025)

Beyond Beats: A Recipe to Song Popularity? A machine learning approach
by: Sebastian, Niklas, et al.
Published: (2024)

Simple Macroeconomic Forecast Distributions for the G7 Economies
by: Becker, Friederike, et al.
Published: (2024)

Stability of clinical prediction models developed using statistical or machine learning methods
by: Riley, Richard D, et al.
Published: (2022)

Uncertainty quantification for data-driven weather models
by: Bülte, Christopher, et al.
Published: (2024)

Electricity price forecasting across Norway's five bidding zones in the post-crisis era
by: Phan, My Thi Diem, et al.
Published: (2026)

Online detection of forecast model inadequacies using forecast errors
by: Grundy, Thomas, et al.
Published: (2025)

mshw, a forecasting library to predict short-term electricity demand based on multiple seasonal Holt-Winters
by: Trull, Oscar, et al.
Published: (2024)

Applications of machine learning to predict seasonal precipitation for East Africa
by: Scheuerer, Michael, et al.
Published: (2024)

Are machine learning interpretations reliable? A stability study on global interpretations
by: Gan, Luqin, et al.
Published: (2025)

Probabilistic Spatial Interpolation of Sparse Data using Diffusion Models
by: Tsao, Valerie, et al.
Published: (2025)

Adaptive probabilistic forecasting of French electricity spot prices
by: Dutot, Grégoire, et al.
Published: (2024)

Efficient mid-term forecasting of hourly electricity load using generalized additive models
by: Zimmermann, Monika, et al.
Published: (2024)

A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting
by: Diao, Tingting, et al.
Published: (2025)

Fast and interpretable electricity consumption scenario generation for individual consumers
by: Soenen, J., et al.
Published: (2024)

Variable importance measure for spatial machine learning models with application to air pollution exposure prediction
by: Cheng, Si, et al.
Published: (2024)

Tackling water table depth modeling via machine learning: From proxy observations to verifiability
by: Janssen, Joseph, et al.
Published: (2024)