Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant

Malaysia's most plentiful renewable energy (RE) source, solar, only generate power during the day. High penetration of intermittent PV generation, especially from Large-Scale Solar Photovoltaic (LSSPV) plants, may complicate power system planning and operation. Hence, the need to accurately for...

Full description

Bibliographic Details
Published in:ENERGY REPORTS
Main Authors: Amer, Hanis Nasuha; Dahlan, Nofri Yenita; Azmi, Azlin Mohd; Latip, Mohd Fuad Abdul; Onn, Mohammad Syazwan; Tumian, Afidalina
Format: Article; Proceedings Paper
Language:English
Published: ELSEVIER 2023
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001131640100047
author Amer
Hanis Nasuha; Dahlan
Nofri Yenita; Azmi
Azlin Mohd; Latip
Mohd Fuad Abdul; Onn
Mohammad Syazwan; Tumian
Afidalina
spellingShingle Amer
Hanis Nasuha; Dahlan
Nofri Yenita; Azmi
Azlin Mohd; Latip
Mohd Fuad Abdul; Onn
Mohammad Syazwan; Tumian
Afidalina
Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant
Energy & Fuels
author_facet Amer
Hanis Nasuha; Dahlan
Nofri Yenita; Azmi
Azlin Mohd; Latip
Mohd Fuad Abdul; Onn
Mohammad Syazwan; Tumian
Afidalina
author_sort Amer
spelling Amer, Hanis Nasuha; Dahlan, Nofri Yenita; Azmi, Azlin Mohd; Latip, Mohd Fuad Abdul; Onn, Mohammad Syazwan; Tumian, Afidalina
Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant
ENERGY REPORTS
English
Article; Proceedings Paper
Malaysia's most plentiful renewable energy (RE) source, solar, only generate power during the day. High penetration of intermittent PV generation, especially from Large-Scale Solar Photovoltaic (LSSPV) plants, may complicate power system planning and operation. Hence, the need to accurately forecast solar generation for sustaining grid stability. This study identified the meteorological variables that significantly impact the power generation of an LSSPV plant using the Pearson Correlation Coefficient (PCC). The LSSPV plant, located in the central area of Malaysia, is used as a case study. To this end, a regression analysis model was developed using Artificial Neural Network (ANN) to predict solar generation with changing meteorological conditions. From August 1, 2020, to July 1, 202,1 and September 1, 2022, to November 1, 2022, a 5-minute dataset is collected. The meteorological variables with the strongest correlation on solar generation were the total global horizontal irradiance and global irradiance on the module plane selected by PCC, followed by PV module temperature, ambient temperature, wind speed, total slope and total horizontal irradiation. The final input to the forecasting model consists of four primary features that indicate a significant correlation with solar PV generation, which are total global horizontal irradiance (W/m(2)), PV module temperature (degrees C), ambient temperature (degrees C) and wind speed (m/s). The simulation findings reveal that the accuracy evaluation metrics are higher, with R-2 metrics improved by 45% when employing feature selection for prediction. While ANN has been commonly used for solar generation prediction, this study uniquely combines ANN with PCC as a feature selection technique. This method has not been widely explored in the literature and has resulted in significant improvements in the accuracy and robustness of the prediction model. Thus, our study provides a valuable contribution to solar energy prediction and can potentially enhance solar energy utilisation efficiency in various applications.
ELSEVIER
2352-4847

2023
9

10.1016/j.egyr.2023.09.141
Energy & Fuels
gold
WOS:001131640100047
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001131640100047
title Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant
title_short Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant
title_full Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant
title_fullStr Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant
title_full_unstemmed Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant
title_sort Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant
container_title ENERGY REPORTS
language English
format Article; Proceedings Paper
description Malaysia's most plentiful renewable energy (RE) source, solar, only generate power during the day. High penetration of intermittent PV generation, especially from Large-Scale Solar Photovoltaic (LSSPV) plants, may complicate power system planning and operation. Hence, the need to accurately forecast solar generation for sustaining grid stability. This study identified the meteorological variables that significantly impact the power generation of an LSSPV plant using the Pearson Correlation Coefficient (PCC). The LSSPV plant, located in the central area of Malaysia, is used as a case study. To this end, a regression analysis model was developed using Artificial Neural Network (ANN) to predict solar generation with changing meteorological conditions. From August 1, 2020, to July 1, 202,1 and September 1, 2022, to November 1, 2022, a 5-minute dataset is collected. The meteorological variables with the strongest correlation on solar generation were the total global horizontal irradiance and global irradiance on the module plane selected by PCC, followed by PV module temperature, ambient temperature, wind speed, total slope and total horizontal irradiation. The final input to the forecasting model consists of four primary features that indicate a significant correlation with solar PV generation, which are total global horizontal irradiance (W/m(2)), PV module temperature (degrees C), ambient temperature (degrees C) and wind speed (m/s). The simulation findings reveal that the accuracy evaluation metrics are higher, with R-2 metrics improved by 45% when employing feature selection for prediction. While ANN has been commonly used for solar generation prediction, this study uniquely combines ANN with PCC as a feature selection technique. This method has not been widely explored in the literature and has resulted in significant improvements in the accuracy and robustness of the prediction model. Thus, our study provides a valuable contribution to solar energy prediction and can potentially enhance solar energy utilisation efficiency in various applications.
publisher ELSEVIER
issn 2352-4847

publishDate 2023
container_volume 9
container_issue
doi_str_mv 10.1016/j.egyr.2023.09.141
topic Energy & Fuels
topic_facet Energy & Fuels
accesstype gold
id WOS:001131640100047
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001131640100047
record_format wos
collection Web of Science (WoS)
_version_ 1809678632389443584