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Increasing the temporal resolution of direct normal solar irradiance forecasted series
Title: Increasing the temporal resolution of direct normal solar irradiance forecasted series
Authors: Fernández-Peruchena, Carlos M.Gastón, MartínSchroedter-Homscheidt, MarionMartínez Marco, Isabel ORCID SCOPUSID Autor AEMETCasado Rubio, José Luis ORCID RESEARCHERID Autor AEMETGarcía-Moya, José AntonioAutor AEMET
Keywords: Solar irradiance; Thermal power plants; Numerical weather prediction; Direct Normal solar Irradiance (DNI); Solar radiation forecasting
Issue Date: 2017
Publisher: American Institute of Physics
Citation: AIP Conference Proceedings. 2017, 1850, p. 140007-1-140007-8
Publisher version:
Abstract: A detailed knowledge of the solar resource is a critical point in the design and control of Concentrating Solar Power (CSP) plants. In particular, accurate forecasting of solar irradiance is essential for the efficient operation of solar thermal power plants, the management of energy markets, and the widespread implementation of this technology. Numerical weather prediction (NWP) models are commonly used for solar radiation forecasting. In the ECMWF deterministic forecasting system, all forecast parameters are commercially available worldwide at 3-hourly intervals. Unfortunately, as Direct Normal solar Irradiance (DNI) exhibits a great variability due to the dynamic effects of passing clouds, 3-h time resolution is insufficient for accurate simulations of CSP plants due to their nonlinear response to DNI, governed by various thermal inertias due to their complex response characteristics. DNI series of hourly or sub-hourly frequency resolution are normally used for an accurate modeling and analysis of transient processes in CSP technologies. In this context, the objective of this study is to propose a methodology for generating synthetic DNI time series at 1-h (or higher) temporal resolution from 3-h DNI series. The methodology is based upon patterns as being defined with help of the clear-sky envelope approach together with a forecast of maximum DNI value, and it has been validated with high quality measured DNI data.
Sponsorship : This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 654984.
ISSN: 0094-243X
Appears in Collections:Artículos científicos 2015-2018

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