An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting

The installation of large-scale solar (LSS) photovoltaic (PV) power plants continues to rise globally as well as in Malaysia. The data provided by LSS PV consist of five weather stations with seven parameters, a 22-unit inverter, and 1-unit PQM Meter Grid as a big dataset. These big data are rapidly...

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Main Authors: Kassim N.M., Santhiran S., Alkahtani A.A., Islam M.A., Tiong S.K., Mohd Yusof M.Y., Amin N.
Other Authors: 57189061699
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Published: Multidisciplinary Digital Publishing Institute (MDPI) 2024
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spelling my.uniten.dspace-340462024-10-14T11:17:46Z An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting Kassim N.M. Santhiran S. Alkahtani A.A. Islam M.A. Tiong S.K. Mohd Yusof M.Y. Amin N. 57189061699 58626157400 55646765500 57657507100 15128307800 58625900900 7102424614 decision tree regression energy forecast global irradiance large-scale solar PV PV plant output Malaysia decision analysis forecasting method irradiance photovoltaic system power plant regression analysis solar power The installation of large-scale solar (LSS) photovoltaic (PV) power plants continues to rise globally as well as in Malaysia. The data provided by LSS PV consist of five weather stations with seven parameters, a 22-unit inverter, and 1-unit PQM Meter Grid as a big dataset. These big data are rapidly changing every minute, they lack data quality when missing data, and need to be analyzed for a longer duration to leverage their benefits to prevent misleading information. This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. Case 1 used all 35 parameters from five weather stations. For Case 2, only seven parameters were used by calculating the mean of five weather stations. While Case 3 was chosen from an index correlation of more than 0.8. The analysis of the historical data was carried out from June 2019 until December 2020. Moreover, the mean absolute error (MAE) was also calculated. A reliability test using the Pearson correlation coefficient (r) and coefficient of determination (R2) was done upon comparing with actual historical data. As a result, Case 2 was proposed to be the best input dataset for the forecasting algorithm. � 2023 by the authors. Final 2024-10-14T03:17:46Z 2024-10-14T03:17:46Z 2023 Article 10.3390/su151813521 2-s2.0-85172888070 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172888070&doi=10.3390%2fsu151813521&partnerID=40&md5=305bf3d16ae9d902840fc7851653ec39 https://irepository.uniten.edu.my/handle/123456789/34046 15 18 13521 All Open Access Gold Open Access Multidisciplinary Digital Publishing Institute (MDPI) Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic decision tree regression
energy
forecast
global irradiance
large-scale solar PV
PV plant output
Malaysia
decision analysis
forecasting method
irradiance
photovoltaic system
power plant
regression analysis
solar power
spellingShingle decision tree regression
energy
forecast
global irradiance
large-scale solar PV
PV plant output
Malaysia
decision analysis
forecasting method
irradiance
photovoltaic system
power plant
regression analysis
solar power
Kassim N.M.
Santhiran S.
Alkahtani A.A.
Islam M.A.
Tiong S.K.
Mohd Yusof M.Y.
Amin N.
An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting
description The installation of large-scale solar (LSS) photovoltaic (PV) power plants continues to rise globally as well as in Malaysia. The data provided by LSS PV consist of five weather stations with seven parameters, a 22-unit inverter, and 1-unit PQM Meter Grid as a big dataset. These big data are rapidly changing every minute, they lack data quality when missing data, and need to be analyzed for a longer duration to leverage their benefits to prevent misleading information. This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. Case 1 used all 35 parameters from five weather stations. For Case 2, only seven parameters were used by calculating the mean of five weather stations. While Case 3 was chosen from an index correlation of more than 0.8. The analysis of the historical data was carried out from June 2019 until December 2020. Moreover, the mean absolute error (MAE) was also calculated. A reliability test using the Pearson correlation coefficient (r) and coefficient of determination (R2) was done upon comparing with actual historical data. As a result, Case 2 was proposed to be the best input dataset for the forecasting algorithm. � 2023 by the authors.
author2 57189061699
author_facet 57189061699
Kassim N.M.
Santhiran S.
Alkahtani A.A.
Islam M.A.
Tiong S.K.
Mohd Yusof M.Y.
Amin N.
format Article
author Kassim N.M.
Santhiran S.
Alkahtani A.A.
Islam M.A.
Tiong S.K.
Mohd Yusof M.Y.
Amin N.
author_sort Kassim N.M.
title An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting
title_short An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting
title_full An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting
title_fullStr An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting
title_full_unstemmed An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting
title_sort adaptive decision tree regression modeling for the output power of large-scale solar (lss) farm forecasting
publisher Multidisciplinary Digital Publishing Institute (MDPI)
publishDate 2024
_version_ 1814061038744633344
score 13.209306