Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm
Abdallah, Mohammed ; Mohammadi, Babak LU
; Nasiri, Hamid
; Katipoğlu, Okan Mert
; Abdalla, Modawy Adam Ali
and Ebadzadeh, Mohammad Mehdi
(2023)
In Energy Reports
10.
p.4198-4217
- Abstract
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... (More)
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).
(Less)
- author
- Abdallah, Mohammed
; Mohammadi, Babak
LU
; Nasiri, Hamid
; Katipoğlu, Okan Mert
; Abdalla, Modawy Adam Ali
and Ebadzadeh, Mohammad Mehdi
- organization
- publishing date
- 2023-11
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Global solar radiation, Meteorological factor, Quantile regression forests, Recurrent fuzzy neural network
- in
- Energy Reports
- volume
- 10
- pages
- 20 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85175724063
- ISSN
- 2352-4847
- DOI
- 10.1016/j.egyr.2023.10.070
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2023 The Authors
- id
- 596be4d9-9f66-4bbc-a297-5bf4916d8836
- date added to LUP
- 2024-01-24 06:11:21
- date last changed
- 2024-01-25 03:21:19
@article{596be4d9-9f66-4bbc-a297-5bf4916d8836,
abstract = {{<p>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).</p>}},
author = {{Abdallah, Mohammed and Mohammadi, Babak and Nasiri, Hamid and Katipoğlu, Okan Mert and Abdalla, Modawy Adam Ali and Ebadzadeh, Mohammad Mehdi}},
issn = {{2352-4847}},
keywords = {{Global solar radiation; Meteorological factor; Quantile regression forests; Recurrent fuzzy neural network}},
language = {{eng}},
pages = {{4198--4217}},
publisher = {{Elsevier}},
series = {{Energy Reports}},
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}},
url = {{http://dx.doi.org/10.1016/j.egyr.2023.10.070}},
doi = {{10.1016/j.egyr.2023.10.070}},
volume = {{10}},
year = {{2023}},
}