Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42405
Title: Solar radiation forecasting with statistical models
Authors: Mazorra-Aguiar, Luis 
Díaz, Felipe 
UNESCO Clasification: 3322 Tecnología energética
Keywords: Irradiance Forecasts
Neural-Networks
Sky Irradiance
Time-Series
Validation, et al
Issue Date: 2018
Journal: Green Energy and Technology 
Abstract: Renewable energy electrical generation has experienced significant growth in the recent years. Renewable energies generate electrical energy using different natural resources, such as solar radiation and wind fields. These resources present an unstable behavior because they depend on different meteorological conditions. In order to maintain the balance between input and output electrical energy into the power system, grid operators need to control and predict these fluctuating events. Indeed, forecasting methods are completely necessary to increase the proportion of renewable energies into the system (Heinemann et al. in Forecasting of solar radiation: solar energy resource management for electricity generation from local level to global scale. Nova Science Publishers, New York, 2006 [17], Wittmann et al. in IEEE J Sel Top Appl Earth Obs Remote Sens 1: 18-27, 2008 [46]). Reducing the uncertainty of natural resources, operators could reduce maintenance costs, improve the interventions in the intra-day market and optimize management decisions with nonrenewable energies supply. Many forecasting methods are used to obtain solar radiation forecasting for different time horizons. In this chapter, we will focus on several solar radiation forecasting statistical methods for intra-day time horizons using ground and exogenous data as inputs.
URI: http://hdl.handle.net/10553/42405
ISBN: 978-3-319-76875-5
ISSN: 1865-3529
DOI: 10.1007/978-3-319-76876-2_8
Source: Green Energy and Technology[ISSN 1865-3529], p. 171-200
Appears in Collections:Capítulo de libro
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