Bayesian methods for electricity load forecasting
type: Dataset
description: In this manuscript, we develop Bayesian statistics tools to forecast the French electricity load. We first prove the asymptotic normality of the posterior distribution (Bernstein-von Mises theorem) for the piecewise linear regression model used to describe the heating effect and the consistency of the Bayes estimator. We then build a a hierarchical informative prior to help improve the quality of the predictions for a high dimension model with a short dataset. We typically show, with two examples involving the non metered EDF customers, that the method we propose allows a more robust estimation of the model with regard to the lack of data. Finally, we study a new nonlinear dynamic model to predict the electricity load online. We develop a particle filter algorithm to estimate the model et compare the predictions obtained with operationnal predictions from EDF.
issued: 2026-05-30T14:25:19.982600
language: en
modified: 2026-05-30T14:25:19.982607
contact point: Organization
  fn: CCSD
keyword: bernstein-von-mises-theorem
keyword: consommation-delectricite
keyword: dynamic-model
keyword: electricity-load
keyword: filtrage-particulaire
keyword: forecasting
keyword: hierarchical-prior-distribution
keyword: historique-court
keyword: infoeu-reposemanticsdoctoralthesis
keyword: loi-a-priori-hierarchique
keyword: modele-dynamique
keyword: particle-filter
keyword: piecewise-linear-regression
keyword: prevision
keyword: regression-lineaire-par-morceaux
keyword: short-dataset
keyword: statapstatistics-statapplications-statap
keyword: theoreme-de-bernstein-von-mises
keyword: theses
landing page: https://theses.hal.science/tel-00766237

Bayesian methods for electricity load forecasting
type: Distribution
format: HTML
issued: 2026-05-30T14:25:20.148504
modified: 2026-05-30T14:25:19.923745
access URL: https://theses.hal.science/tel-00766237

test_moissonnage_selune
type: Agent