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

0456363 - ÚVGZ 2016 RIV CZ eng C - Conference Paper (international conference)
Rajdl, Kamil - Farda, Aleš - Štěpánek, Petr - Zahradníček, Pavel
Testing a statistical forecasting model of electric energy consumption for two regions in the Czech Republic.
Global Change: A Complex Challenge : Conference Proceedings. Brno: Global Change Research Centre, The Czech Academy of Sciences, v. v. i., 2015 - (Urban, O.; Šprtová, M.; Klem, K.), s. 178-181. ISBN 978-80-87902-10-3.
[Global Change: A Complex Challenge /4th/. Brno (CZ), 23.03.2015-24.03.2015]
R&D Projects: GA MŠMT(CZ) LO1415
Institutional support: RVO:67179843
Keywords : forecasting model * electric energy * Czech Republic
Subject RIV: EH - Ecology, Behaviour

Precise 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
Permanent Link: http://hdl.handle.net/11104/0256911