@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00766237v1> a dcat:Dataset ;
    dct: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.
            """ ;
    dct:identifier "tel-00766237" ;
    dct:issued "2026-05-30T14:25:19.982600"^^xsd:dateTime ;
    dct:language "en" ;
    dct:modified "2026-05-30T14:25:19.982607"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Bayesian methods for electricity load forecasting" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00766237v1/resource/57e7e999-efa7-490f-b6ec-d2ed0458a820> ;
    dcat:keyword "bernstein-von-mises-theorem",
        "consommation-delectricite",
        "dynamic-model",
        "electricity-load",
        "filtrage-particulaire",
        "forecasting",
        "hierarchical-prior-distribution",
        "historique-court",
        "infoeu-reposemanticsdoctoralthesis",
        "loi-a-priori-hierarchique",
        "modele-dynamique",
        "particle-filter",
        "piecewise-linear-regression",
        "prevision",
        "regression-lineaire-par-morceaux",
        "short-dataset",
        "statapstatistics-statapplications-statap",
        "theoreme-de-bernstein-von-mises",
        "theses" ;
    dcat:landingPage <https://theses.hal.science/tel-00766237> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00766237v1/resource/57e7e999-efa7-490f-b6ec-d2ed0458a820> a dcat:Distribution ;
    dct:format "HTML" ;
    dct:issued "2026-05-30T14:25:20.148504"^^xsd:dateTime ;
    dct:modified "2026-05-30T14:25:19.923745"^^xsd:dateTime ;
    dct:title "Bayesian methods for electricity load forecasting" ;
    dcat:accessURL <https://theses.hal.science/tel-00766237> .

<https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> a foaf:Agent ;
    foaf:name "test_moissonnage_selune" .

<https://theses.hal.science/tel-00766237> a foaf:Document .