Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm
Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode d...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723015068 |
| _version_ | 1797378710052012032 |
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| author | Mohammed Abdallah Babak Mohammadi Hamid Nasiri Okan Mert Katipoğlu Modawy Adam Ali Abdalla Mohammad Mehdi Ebadzadeh |
| author_facet | Mohammed Abdallah Babak Mohammadi Hamid Nasiri Okan Mert Katipoğlu Modawy Adam Ali Abdalla Mohammad Mehdi Ebadzadeh |
| author_sort | Mohammed Abdallah |
| collection | DOAJ |
| description | Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode decomposition (VMD) with a multi-functional recurrent fuzzy neural network (MFRFNN) and quantile regression forests (QRF) models for GSR prediction in daily scales. The hybrid VMD-MFRFNN and QRF models were compared with standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), and M5 tree (M5T) models across the Lund and Växjö meteorological stations in Sweden. The meteorological data from 2008 to 2017 were used to train the models, while the prediction accuracy was verified by using the data from 2018 to 2021 under five different input combinations. The various meteorological-based scenarios (including the input are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), and maximum possible sunshine duration (N)) were considered as input of predictor models. The current study resulted that the M5T model exhibited higher accuracy than RF and XGB models, while the QRF model showed equivalent performance with the M5T model at both study sites. The MFRFNN model outperformed QRF and M5T models across all input combinations at both study sites. The hybrid VMD-MFRFNN model showed the best performance when fewer input variables (Tmin, Tmax, T, WS at Lund station and Tmin, Tmax, T, WS, SSH, RH at Växjö station) were used for GSR prediction. We conclude that the MFRFNN model best predicts average daily GSR when combining all meteorological variables (Tmin, Tmax, T, WS, SSH, RH, N). |
| first_indexed | 2024-03-08T20:10:37Z |
| format | Article |
| id | doaj.art-e412a8ac70234600bd33525cc7814f55 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T20:10:37Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-e412a8ac70234600bd33525cc7814f552023-12-23T05:22:04ZengElsevierEnergy Reports2352-48472023-11-011041984217Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithmMohammed Abdallah0Babak Mohammadi1Hamid Nasiri2Okan Mert Katipoğlu3Modawy Adam Ali Abdalla4Mohammad Mehdi Ebadzadeh5College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu 210024, China; The Hydraulics Research Station, PO Box 318, Wad Medani, SudanDepartment of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden; Corresponding author.Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, IranDepartment of Civil Engineering, Erzincan Binali Yıldırım University, Erzincan, TurkeyCollege of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China; Department of Electrical and Electronic Engineering, College of Engineering Science, Nyala University, Nyala 63311, SudanDepartment of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, IranGlobal solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode decomposition (VMD) with a multi-functional recurrent fuzzy neural network (MFRFNN) and quantile regression forests (QRF) models for GSR prediction in daily scales. The hybrid VMD-MFRFNN and QRF models were compared with standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), and M5 tree (M5T) models across the Lund and Växjö meteorological stations in Sweden. The meteorological data from 2008 to 2017 were used to train the models, while the prediction accuracy was verified by using the data from 2018 to 2021 under five different input combinations. The various meteorological-based scenarios (including the input are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), and maximum possible sunshine duration (N)) were considered as input of predictor models. The current study resulted that the M5T model exhibited higher accuracy than RF and XGB models, while the QRF model showed equivalent performance with the M5T model at both study sites. The MFRFNN model outperformed QRF and M5T models across all input combinations at both study sites. The hybrid VMD-MFRFNN model showed the best performance when fewer input variables (Tmin, Tmax, T, WS at Lund station and Tmin, Tmax, T, WS, SSH, RH at Växjö station) were used for GSR prediction. We conclude that the MFRFNN model best predicts average daily GSR when combining all meteorological variables (Tmin, Tmax, T, WS, SSH, RH, N).http://www.sciencedirect.com/science/article/pii/S2352484723015068Global solar radiationRecurrent fuzzy neural networkQuantile regression forestsMeteorological factor |
| spellingShingle | Mohammed Abdallah Babak Mohammadi Hamid Nasiri Okan Mert Katipoğlu Modawy Adam Ali Abdalla Mohammad Mehdi Ebadzadeh Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm Energy Reports Global solar radiation Recurrent fuzzy neural network Quantile regression forests Meteorological factor |
| title | Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm |
| title_full | Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm |
| title_fullStr | Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm |
| title_full_unstemmed | Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm |
| title_short | Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm |
| title_sort | daily global solar radiation time series prediction using variational mode decomposition combined with multi functional recurrent fuzzy neural network and quantile regression forests algorithm |
| topic | Global solar radiation Recurrent fuzzy neural network Quantile regression forests Meteorological factor |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723015068 |
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