Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, Philippines
In recent years, solar radiation forecasting has become highly important worldwide as solar energy increases its contribution to electricity grids. However, due to the intermittent nature of solar radiation caused by meteorological parameters, forecasting errors arise, and fluctuations in the power...
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| Format: | Article |
| Language: | English |
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Diponegoro University
2022-08-01
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| Series: | International Journal of Renewable Energy Development |
| Subjects: | |
| Online Access: | https://ijred.cbiore.id/index.php/ijred/article/view/44755 |
| _version_ | 1797449765911265280 |
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| author | Al Diego Pega Fuselero Hannah Mae San Agustin Portus Bonifacio Tobias Doma Jr |
| author_facet | Al Diego Pega Fuselero Hannah Mae San Agustin Portus Bonifacio Tobias Doma Jr |
| author_sort | Al Diego Pega Fuselero |
| collection | DOAJ |
| description | In recent years, solar radiation forecasting has become highly important worldwide as solar energy increases its contribution to electricity grids. However, due to the intermittent nature of solar radiation caused by meteorological parameters, forecasting errors arise, and fluctuations in the power output of photovoltaic (PV) systems become a severe issue. This paper aims to introduce a forecasting hybrid model of daily global solar radiation time series. Meteorological data and solar radiation samples from Dumaguete, Philippines, are used to assess the forecasting accuracy of the proposed nonlinear autoregressive network with exogenous inputs (NARX) – gated recurrent unit (GRU) hybrid model. Four different models were trained using the meteorological and solar radiation data, which are the Optimizable Gaussian Process Regression (GPR), Nonlinear Autoregressive Network (NAR), NARX, and the proposed Hybrid NARX-GRU Network. Results show that the hybrid NARX-GRU model has a root mean square error (RMSE) of ~0.05 and a training time of 33 seconds. The proposed hybrid model has better forecasting performance compared to the three models which obtained RMSE values of 27.741, 39.82, and 28.92, for the GPR, NAR, and NARX, respectively. The simulation results demonstrate that the NARX-GRU model significantly outperforms the regression and single models in terms of statistical metrics and training efficiency. Furthermore, this study shows that the hybridized NARX-GRU model is able to provide an effective estimation for daily global solar radiation, which is important in the operation of PV plants in the country, specifically for unit commitment purposes |
| first_indexed | 2024-03-09T14:30:47Z |
| format | Article |
| id | doaj.art-6d729c60ffab49d0bb13e6495671ce70 |
| institution | Directory Open Access Journal |
| issn | 2252-4940 |
| language | English |
| last_indexed | 2024-03-09T14:30:47Z |
| publishDate | 2022-08-01 |
| publisher | Diponegoro University |
| record_format | Article |
| series | International Journal of Renewable Energy Development |
| spelling | doaj.art-6d729c60ffab49d0bb13e6495671ce702023-11-28T02:08:37ZengDiponegoro UniversityInternational Journal of Renewable Energy Development2252-49402022-08-0111383985010.14710/ijred.2022.4475520454Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, PhilippinesAl Diego Pega Fuselero0Hannah Mae San Agustin Portus1Bonifacio Tobias Doma Jr2https://orcid.org/0000-0002-5537-2018School of Chemical, Biological, and Materials Engineering and Sciences, Mapúa University, Manila 1002, PhilippinesSchool of Chemical, Biological, and Materials Engineering and Sciences, Mapúa University, Manila 1002, PhilippinesSchool of Chemical, Biological, and Materials Engineering and Sciences, Mapúa University, Manila 1002, PhilippinesIn recent years, solar radiation forecasting has become highly important worldwide as solar energy increases its contribution to electricity grids. However, due to the intermittent nature of solar radiation caused by meteorological parameters, forecasting errors arise, and fluctuations in the power output of photovoltaic (PV) systems become a severe issue. This paper aims to introduce a forecasting hybrid model of daily global solar radiation time series. Meteorological data and solar radiation samples from Dumaguete, Philippines, are used to assess the forecasting accuracy of the proposed nonlinear autoregressive network with exogenous inputs (NARX) – gated recurrent unit (GRU) hybrid model. Four different models were trained using the meteorological and solar radiation data, which are the Optimizable Gaussian Process Regression (GPR), Nonlinear Autoregressive Network (NAR), NARX, and the proposed Hybrid NARX-GRU Network. Results show that the hybrid NARX-GRU model has a root mean square error (RMSE) of ~0.05 and a training time of 33 seconds. The proposed hybrid model has better forecasting performance compared to the three models which obtained RMSE values of 27.741, 39.82, and 28.92, for the GPR, NAR, and NARX, respectively. The simulation results demonstrate that the NARX-GRU model significantly outperforms the regression and single models in terms of statistical metrics and training efficiency. Furthermore, this study shows that the hybridized NARX-GRU model is able to provide an effective estimation for daily global solar radiation, which is important in the operation of PV plants in the country, specifically for unit commitment purposeshttps://ijred.cbiore.id/index.php/ijred/article/view/44755forecastingnarx-gruneural networkphotovoltaic systemsolar radiation |
| spellingShingle | Al Diego Pega Fuselero Hannah Mae San Agustin Portus Bonifacio Tobias Doma Jr Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, Philippines International Journal of Renewable Energy Development forecasting narx-gru neural network photovoltaic system solar radiation |
| title | Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, Philippines |
| title_full | Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, Philippines |
| title_fullStr | Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, Philippines |
| title_full_unstemmed | Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, Philippines |
| title_short | Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, Philippines |
| title_sort | daily solar radiation forecasting based on a hybrid narx gru network in dumaguete philippines |
| topic | forecasting narx-gru neural network photovoltaic system solar radiation |
| url | https://ijred.cbiore.id/index.php/ijred/article/view/44755 |
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