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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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 |
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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 |
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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. |
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57189061699 |
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57189061699 Kassim N.M. Santhiran S. Alkahtani A.A. Islam M.A. Tiong S.K. Mohd Yusof M.Y. Amin N. |
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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 |
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1814061038744633344 |
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13.209306 |