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Testing a statistical forecasting model of electric energy consumption for two regions in the Czech Republic

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    SYSNO ASEP0456363
    Document TypeC - Proceedings Paper (int. conf.)
    R&D Document TypeConference Paper
    TitleTesting a statistical forecasting model of electric energy consumption for two regions in the Czech Republic
    Author(s) Rajdl, Kamil (UEK-B) RID
    Farda, Aleš (UEK-B) RID, SAI
    Štěpánek, Petr (UEK-B) RID, SAI, ORCID
    Zahradníček, Pavel (UEK-B) RID, SAI
    Source TitleGlobal Change: A Complex Challenge : Conference Proceedings. - Brno : Global Change Research Centre, The Czech Academy of Sciences, v. v. i., 2015 / Urban Otmar ; Šprtová Mirka ; Klem Karel - ISBN 978-80-87902-10-3
    Pagess. 178-181
    Number of pages4 s.
    Publication formPrint - P
    ActionGlobal Change: A Complex Challenge /4th/
    Event date23.03.2015-24.03.2015
    VEvent locationBrno
    CountryCZ - Czech Republic
    Event typeWRD
    Languageeng - English
    CountryCZ - Czech Republic
    Keywordsforecasting model ; electric energy ; Czech Republic
    Subject RIVEH - Ecology, Behaviour
    R&D ProjectsLO1415 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
    Institutional supportRVO:67179843 - RVO:67179843
    UT WOS000381161600043
    AnnotationPrecise forecasting of electric energy consumption is of great importance for the electric power industry. It helps system operators optimally schedule and control power systems, and even slight improvements in prediction accuracy might yield large savings or profits. For these reasons, many forecasting models based on various principles have been developed and studied. Because of energy consumption’s strong dependence on weather conditions, such models often utilize outputs from numerical weather prediction models. In this study, we present and analyse a statistical model for forecasting hourly electrical energy consumption by customers of E.ON Energie, a.s. in two regions of the Czech Republic. The aim of this model is to create hourly predictions up to several days in advance. The model uses hourly data of consumed energy from 2011–2014 and corresponding predictions of temperature and cloudiness provided by the ALADIN/ CZ model. The statistical model is based on a regression analysis applied to appropriate data samples and supplemented by several optional post-processing methods. Specifically, we use a robust linear regression algorithm to identify energy consumption’s dependence on temperature, the meteorological variable with the largest influence on consumption. Our post-processing methods focused on removing prediction bias resulting from economic situations (represented by the goss domestic product, GDP) and sudden temperature changes. We analysed the presented model from the point of view of the hourly predictions’ accuracy for 2013 and 2014. Accuracy was primarily measured by mean absolute error. It was evaluated for individual months, and the effects of individual parts of the model on accuracy value are shown. Introduction
    WorkplaceGlobal Change Research Institute
    ContactNikola Šviková, svikova.n@czechglobe.cz, Tel.: 511 192 268
    Year of Publishing2016
Number of the records: 1  

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