Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil

OLIVEIRA, Nadja Gomes de. Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil. 2022. 77 f. Dissertação (Mestrado em Engenharia Mecânica) – Universidade Federal do Ceará, Centro de Tecnologia, Programa de Pós-Graduação em Engenhar...

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Detalles Bibliográficos
Autor: Oliveira, Nadja Gomes de
Tipo de recurso: tesis de maestría
Estado:Versión publicada
Fecha de publicación:2022
País:Brasil
Institución:Universidade Federal do Ceará (UFC)
Repositorio:Repositório Institucional da Universidade Federal do Ceará (UFC)
Idioma:portugués
OAI Identifier:oai:repositorio.ufc.br:riufc/68017
Acceso en línea:http://www.repositorio.ufc.br/handle/riufc/68017
Access Level:acceso abierto
Palabra clave:Machine learning
Global solar irradiance
Direct normal irradiance
Intra-hour forecasting
Caret R package
Inteligência artificial
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dc.title.pt_BR.fl_str_mv Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil
dc.title.en.pt_BR.fl_str_mv Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil
title Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil
spellingShingle Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil
Oliveira, Nadja Gomes de
Machine learning
Global solar irradiance
Direct normal irradiance
Intra-hour forecasting
Caret R package
Inteligência artificial
title_short Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil
title_full Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil
title_fullStr Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil
title_full_unstemmed Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil
title_sort Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil
dc.creator.none.fl_str_mv Oliveira, Nadja Gomes de
author Oliveira, Nadja Gomes de
author_facet Oliveira, Nadja Gomes de
author_role author
dc.contributor.advisor1.fl_str_mv Rocha, Paulo Alexandre Costa
contributor_str_mv Rocha, Paulo Alexandre Costa
dc.subject.por.fl_str_mv Machine learning
Global solar irradiance
Direct normal irradiance
Intra-hour forecasting
Caret R package
Inteligência artificial
topic Machine learning
Global solar irradiance
Direct normal irradiance
Intra-hour forecasting
Caret R package
Inteligência artificial
description OLIVEIRA, Nadja Gomes de. Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil. 2022. 77 f. Dissertação (Mestrado em Engenharia Mecânica) – Universidade Federal do Ceará, Centro de Tecnologia, Programa de Pós-Graduação em Engenharia Mecânica, Fortaleza, 2022.
publishDate 2022
dc.date.accessioned.fl_str_mv 2022-09-05T13:36:00Z
dc.date.available.fl_str_mv 2022-09-05T13:36:00Z
dc.date.issued.fl_str_mv 2022-09-29
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.citation.fl_str_mv OLIVEIRA, N. G. (2022)
dc.identifier.uri.fl_str_mv http://www.repositorio.ufc.br/handle/riufc/68017
identifier_str_mv OLIVEIRA, N. G. (2022)
url http://www.repositorio.ufc.br/handle/riufc/68017
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da Universidade Federal do Ceará (UFC)
instname:Universidade Federal do Ceará (UFC)
instacron:UFC
instname_str Universidade Federal do Ceará (UFC)
instacron_str UFC
institution UFC
reponame_str Repositório Institucional da Universidade Federal do Ceará (UFC)
collection Repositório Institucional da Universidade Federal do Ceará (UFC)
bitstream.url.fl_str_mv http://repositorio.ufc.br/bitstream/riufc/68017/3/2022_dis_ngoliveira.pdf
http://repositorio.ufc.br/bitstream/riufc/68017/4/license.txt
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bitstream.checksumAlgorithm.fl_str_mv MD5
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repository.name.fl_str_mv Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)
repository.mail.fl_str_mv bu@ufc.br || repositorio@ufc.br
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spelling Rocha, Paulo Alexandre Costa2022-09-05T13:36:00Z2022-09-05T13:36:00Z2022-09-29OLIVEIRA, N. G. (2022)http://www.repositorio.ufc.br/handle/riufc/68017OLIVEIRA, Nadja Gomes de. Evaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazil. 2022. 77 f. Dissertação (Mestrado em Engenharia Mecânica) – Universidade Federal do Ceará, Centro de Tecnologia, Programa de Pós-Graduação em Engenharia Mecânica, Fortaleza, 2022.This work uses the SONDA network irradiance data to forecast global horizontal and direct normal irradiances (GHI and DNI) intra-hourly applying 5min and 60min forecast window resolution and five different time horizons (5min, 30min, 60min, 6 hours and 12 hours) during the period of four years for a solarimetric and anemometric station in the northeast of Brazil, Petrolina/PE. Five different machine learning models were tested, namely: Multivariate Adaptive Regression Splines (MARS), Least Absolute Shrinkage and Selection Operator (LASSO), k-nearest neighbors (kNN), Extreme Gradient Boosting (XGBoost) and an ensemble combination to form a final forecast (Ensemble with Ridge Regression). Their performance was compared using the RMSE and forecast skill (FS) relative to the smart persistence model. Results show that the machine learning models achieve significant forecast improvements over the reference model using only endogenous features. In addition, the Ensemble with Ridge Regression and XGBoost models have rarely been used for very short-term solar forecasting according to the literature. This framework can be used to select appropriate machine learning approaches for very short-term solar power forecasting and the simulation results can be used as a baseline for comparison. The XGBoost’s forecast skill model was not the winner in all time horizons and resolutions, but it is among the best results for GHI and DNI, with normalized variables. The XGBoost model prevails when the time resolution of 5 min is chosen, not considering other error metrics, such as MBE. It is worth to mention, for the time resolution of 5 min, that the XGBoost model has the best FS results in 66.66% of the time comparing to all the six results for GHI and DNI with raw and normalized variables. For the time resolution of 60 min, the MARS model has the best forecast skill’s results, dominating around 66.66% of all the outputs, including GHI and DNI for raw and normalized variables. Also, kNN is the Machine Learning model with the best outputs of MBE, proving that the model is more accurate and does not have huge estimations variations comparing to the other models.Machine learningGlobal solar irradianceDirect normal irradianceIntra-hour forecastingCaret R packageInteligência artificialEvaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, BrazilEvaluation of machine learning models for solar irradiance prediction (GHI and DNI): a case study in Petrolina, PE, Brazilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisporreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFCinfo:eu-repo/semantics/openAccessOliveira, Nadja Gomes deORIGINAL2022_dis_ngoliveira.pdf2022_dis_ngoliveira.pdfDissertação de Nadja Gomes de Oliveiraapplication/pdf3802405http://repositorio.ufc.br/bitstream/riufc/68017/3/2022_dis_ngoliveira.pdff46e0930e23763e1c217c8d1bd2166d7MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-82152http://repositorio.ufc.br/bitstream/riufc/68017/4/license.txtfb3ad2d23d9790966439580114baefafMD54riufc/680172022-09-05 10:36:00.187oai:repositorio.ufc.br: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Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2022-09-05T13:36Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false
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