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

Author

Listed:
  • Nabilah Mat Kassim

    (Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia)

  • Sathiswary Santhiran

    (Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia)

  • Ammar Ahmed Alkahtani

    (Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia)

  • Mohammad Aminul Islam

    (Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Kuala Lumpur, Malaysia)

  • Sieh Kiong Tiong

    (Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia)

  • Mohd Yusrizal Mohd Yusof

    (TNB Renewables Sdn. Bhd., No. 16A, Persiaran Barat, Petaling Jaya 46050, Selangor, Malaysia)

  • Nowshad Amin

    (Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia)

Abstract

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 (R 2 ) 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.

Suggested Citation

  • Nabilah Mat Kassim & Sathiswary Santhiran & Ammar Ahmed Alkahtani & Mohammad Aminul Islam & Sieh Kiong Tiong & Mohd Yusrizal Mohd Yusof & Nowshad Amin, 2023. "An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting," Sustainability, MDPI, vol. 15(18), pages 1-12, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13521-:d:1236626
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    References listed on IDEAS

    as
    1. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Probabilistic solar power forecasting based on weather scenario generation," Applied Energy, Elsevier, vol. 266(C).
    2. Wu, Wei & Tang, Xiaoping & Lv, Jiake & Yang, Chao & Liu, Hongbin, 2021. "Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas," Renewable Energy, Elsevier, vol. 177(C), pages 148-163.
    3. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
    4. Md Mijanur Rahman & Mohammad Shakeri & Sieh Kiong Tiong & Fatema Khatun & Nowshad Amin & Jagadeesh Pasupuleti & Mohammad Kamrul Hasan, 2021. "Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
    5. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
    6. Wan Syakirah Wan Abdullah & Miszaina Osman & Mohd Zainal Abidin Ab Kadir & Renuga Verayiah, 2019. "The Potential and Status of Renewable Energy Development in Malaysia," Energies, MDPI, vol. 12(12), pages 1-16, June.
    7. Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.
    8. Ciulla, G. & D'Amico, A. & Lo Brano, V. & Traverso, M., 2019. "Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level," Energy, Elsevier, vol. 176(C), pages 380-391.
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