Cover Image

Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Das, Abhinav, Schlüter, Stephan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning G.3
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices
by: Nickelsen, Daniel, et al.
Published: (2024)

Regime-Aware Conditional Neural Processes with Multi-Criteria Decision Support for Operational Electricity Price Forecasting
by: Das, Abhinav, et al.
Published: (2025)

Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting
by: Lebedev, Andreas, et al.
Published: (2025)

Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression
by: Das, Abhinav, et al.
Published: (2024)

Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting
by: Lettner, Jan Niklas, et al.
Published: (2026)

Beyond NNGP: Large Deviations and Feature Learning in Bayesian Neural Networks
by: Papagiannouli, Katerina, et al.
Published: (2026)

Last Layer Hamiltonian Monte Carlo
by: Vellenga, Koen, et al.
Published: (2025)

Day-Ahead Electricity Price Forecasting for Volatile Markets Using Foundation Models with Regularization Strategy
by: Ponyuenyong, Kritchanat, et al.
Published: (2026)

Interval Forecasts for Gas Prices in the Face of Structural Breaks -- Statistical Models vs. Neural Networks
by: Schlüter, Stephan, et al.
Published: (2024)

Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural Networks
by: Ahmed, Soyed Tuhin, et al.
Published: (2024)

Dirichlet-Based Monte Carlo Dropout for Uncertainty Estimation in Neural Networks
by: Hoblos, Rouaa, et al.
Published: (2026)

Optimal Control of a Battery Storage On the Energy Market
by: Schlüter, Stephan, et al.
Published: (2024)

RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting
by: Lv, Haochen, et al.
Published: (2025)

Assessing Electricity Demand Forecasting with Exogenous Data in Time Series Foundation Models
by: Cheong, Wei Soon, et al.
Published: (2026)

Uncertainty Propagation Networks for Neural Ordinary Differential Equations
by: Jahanshahi, Hadi, et al.
Published: (2025)

Random Feature Spiking Neural Networks
by: Gollwitzer, Maximilian, et al.
Published: (2025)

Bayesian Optimization in Linear Time
by: Schneider, Jesse, et al.
Published: (2026)

Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting
by: Amor, Souhir Ben, et al.
Published: (2025)

A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset
by: Muhammad, Tashreef, et al.
Published: (2026)

Unreliable Uncertainty Estimates with Monte Carlo Dropout
by: Djupskås, Aslak, et al.
Published: (2025)

Empirical evaluation of normalizing flows in Markov Chain Monte Carlo
by: Nabergoj, David, et al.
Published: (2024)

A Distribution-to-Distribution Neural Probabilistic Forecasting Framework for Dynamical Systems
by: Yang, Tianlin, et al.
Published: (2026)

Robust Domain Generalisation with Causal Invariant Bayesian Neural Networks
by: Gendron, Gaël, et al.
Published: (2024)

SmallML: Bayesian Transfer Learning for Small-Data Predictive Analytics
by: Leontev, Semen
Published: (2025)

Partial Trace-Class Bayesian Neural Networks
by: Carter, Arran, et al.
Published: (2025)

ProBoost: a Boosting Method for Probabilistic Classifiers
by: Mendonça, Fábio, et al.
Published: (2022)

A Hybrid Model for Stock Market Forecasting: Integrating News Sentiment and Time Series Data with Graph Neural Networks
by: Sadek, Nader, et al.
Published: (2025)

Bandwidth Selectors on Semiparametric Bayesian Networks
by: Alejandre, Victor, et al.
Published: (2025)

A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data
by: Ziaei, Navid, et al.
Published: (2024)

Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning
by: Mahtout, Btissame El, et al.
Published: (2026)

Replicable Distribution Testing
by: Diakonikolas, Ilias, et al.
Published: (2025)

Identifying Information from Observations with Uncertainty and Novelty
by: Prijatelj, Derek S., et al.
Published: (2025)

FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment
by: Yasuno, Takato
Published: (2026)

Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problem
by: Popov, Roumen Nikolaev
Published: (2024)

Random Models for Fuzzy Clustering Similarity Measures
by: DeWolfe, Ryan, et al.
Published: (2023)

ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation
by: Borchers, Conrad, et al.
Published: (2024)

An Iterative Bayesian Approach for System Identification based on Linear Gaussian Models
by: Tzikas, Alexandros E., et al.
Published: (2025)

Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network
by: Innocenti, Lorenzo, et al.
Published: (2024)

Going NUTS with ADVI: Exploring various Bayesian Inference techniques with Facebook Prophet
by: Krajevski, Jovan, et al.
Published: (2026)

A Robust Monotonic Single-Index Model for Skewed and Heavy-Tailed Data: A Deep Neural Network Approach Applied to Periodontal Studies
by: Liu, Qingyang, et al.
Published: (2025)