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Title in Portuguese: Radial Basis Function for Solar Irradiance Forecasting in Equatorial Areas
Title: Radial Basis Function for Solar Irradiance Forecasting in Equatorial Areas
Author: Lima, Marcello Anderson Ferreira Batista
Carvalho, Paulo Cesar Marques de
Braga, Arthur Plínio de Souza
Pereira, Renata Imaculada Soares
Jucá, Sandro César Silveira
Fernández Ramírez, Luis Miguel
Leite, Josileudo Rodrigues
Keywords: Solar forecast
Solar energy
Artificial neural networks
Radial base function
Issue Date: 2019
Citation: LIMA, Marcello Anderson Ferreira Batista; CARVALHO, Paulo Cesar Marques de; BRAGA, Arthur Plínio de Souza; PEREIRA, Renata Imaculada Soares; JUCÁ, Sandro César Silveira; FERNÁNDEZ RAMÍREZ, Luis Miguel; LEITE, Josileudo Rodrigues. Radial basis function for solar irradiance forecasting in equatorial areas. In: INTERNATIONAL CONFERENCE ON RENEWABLE ENERGIES AND POWER QUALITY(ICREPQ'19), 17th., 10th to 12th April, 2019, Tenerife, Spain, 2019. Renewable Energy and Power Quality Journal (RE&PQJ), n.17, p.280-287, July 2019. REF: 288-19, DOI:10.24084/repqj17.288
Abstract: Photovoltaic (PV) solar generation is gaining an increasing attention due to technological advances such as higher efficiency and life of PV cells and cost reduction. Due to its vast territory, Brazil is composed of regions that can explore renewable energy sources for electricity generation, and the solar resource is found satisfactorily in several areas of the country. This article presents a solar irradiance prediction mechanism developed using data collected in Fortaleza-CE, Brazil. Due to the fact of its characteristic of unpredictability for this resource, many researchers look for several methods to take the generation of this type of energy. The predictions were performed using a Radial Basis Function (RBF) a computational model based on the human nervous system, it is a technical and effective for time series forecasting, which is a relatively complex problem, Artificial Neural Network (ANN) with the advancement of 1 hour. In the ANN performance, a total of 34.4% forecasts underestimated solar energy availability, 7% of the forecasts obtained error 0 and 58.6% of forecasts overestimated the solar resource. A total of 62.33% of forecasts was between -10% and 10% of forecast error. The prediction mean error was 5.93% and the Mean Absolute Percentage Error (MAPE) was 11.43%.
metadata.dc.type: Artigo de Periódico
ISSN: 2172-038X
metadata.dc.identifier.other: DOI:10.24084/repqj17.288
REF: 288-19
Appears in Collections:DEEL - Artigos publicados em revista científica

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