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Short-term solar irradiation forecasting based on Dynamic Harmonic Regression
Universidad de Castilla-La Mancha, Departamento de Administracion de Empresas, Ciudad Real, Spain.
Lancaster University, Department of Management Science, United Kingdom.ORCID iD: 0000-0003-0211-5218
Universidad de Castilla-La Mancha, Departamento de Administracion de Empresas, Ciudad Real, Spain.
2015 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 84, p. 289-295Article in journal (Refereed) Published
Abstract [en]

Solar power generation is a crucial research area for countries that have high dependency on fossil energy sources and is gaining prominence with the current shift to renewable sources of energy. In order to integrate the electricity generated by solar energy into the grid, solar irradiation must be reasonably well forecasted, where deviations of the forecasted value from the actual measured value involve significant costs. The present paper proposes a univariate Dynamic Harmonic Regression model set up in a State Space framework for short-term (1-24h) solar irradiation forecasting. Time series hourly aggregated as the Global Horizontal Irradiation and the Direct Normal Irradiation will be used to illustrate the proposed approach. This method provides a fast automatic identification and estimation procedure based on the frequency domain. Furthermore, the recursive algorithms applied offer adaptive predictions. The good forecasting performance is illustrated with solar irradiance measurements collected from ground-based weather stations located in Spain. The results show that the Dynamic Harmonic Regression achieves the lowest relative Root Mean Squared Error; about 30% and 47% for the Global and Direct irradiation components, respectively, for a forecast horizon of 24h ahead. 

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 84, p. 289-295
Keywords [en]
Dynamic harmonic regression, Exponential smoothing, Forecasting, Solar irradiation, Unobserved components model, Automation, Frequency domain analysis, Frequency estimation, Harmonic analysis, Mean square error, Radiation, Regression analysis, Solar energy, Solar power generation, Solar radiation, Video signal processing, Automatic identification, Dynamic harmonic regressions, Forecasting performance, Root mean squared errors, Solar irradiance measurement, Unobserved components, Irradiation, electricity generation, error analysis, estimation method, solar power, weather station, Spain
National Category
Probability Theory and Statistics Energy Systems Energy Engineering
Identifiers
URN: urn:nbn:se:his:diva-18249DOI: 10.1016/j.energy.2015.02.100ISI: 000355035900028Scopus ID: 2-s2.0-84928429252OAI: oai:DiVA.org:his-18249DiVA, id: diva2:1402757
Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-03-02Bibliographically approved

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Kourentzes, Nikolaos

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