Forecasting Produksi Energi Photovoltaic Menggunakan Algoritma Random Forest Classification
-
Ergi Putra Febtiawan
*
Universitas Mataram, Mataram,
Indonesia
- Lalu A. Syamsul Irfan Akbar Universitas Mataram, Mataram, Indonesia
- A Sjamsjiar Rachman Universitas Mataram, Mataram, Indonesia (*) Corresponding Author
Abstract
Solar energy is one of the most affordable energy sources and a clean and friendly renewable energy source in the world. Solar energy, which is abundant throughout the world, can be an energy source that is economical to implement. Due to its advantages, the use of solar energy continues to increase throughout the year. One way to convert solar energy into electrical energy is to use a Photovoltaic (PV) or PV Module device. The PV module used as a study is located in the PLN PLTS 7 MWp in Sengkol village. The PV module used as a study is located in the PLN PLTS 7 MWp in Sengkol village. PV energy production forecasting is carried out to assist in managing planning and knowing PV energy production based on meteorological data in an area where PV equipment has not been installed with a user interface in the form of a website. Machine learning is a technique that allows machines to learn directly from examples, data and experience. In contrast to traditional programming approaches where machines are given commands one by one, machine learning can make its own decisions on a problem after it "learns" from the examples or data provided. This research attempts to forecast the energy production produced by photovoltaic devices using the Random Forest Classification algorithm, which is a classification algorithm from machine learning, so that the overall performance results of this modeling are obtained for the data provided. This algorithm works by collecting predictions from a large number of independent decision trees, and then combining these predictions to produce a more accurate and stable final result by involving the use of historical data about temperature, solar radiation, solar radiation, and other environmental characteristics to predict the amount of energy produced by the photovoltaic system at a certain time, so that the overall performance results of this model are obtained for the data provided. The model built was evaluated using a confusion matrix and the results showed that this algorithm achieved an accuracy level of 96%. These results indicate that Random Forest Classification is an effective and reliable method for forecasting photovoltaic energy production.
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References
H. B. Nurjaman and T. Purnama, “Pembangkit Listrik Tenaga Surya (PLTS) Sebagai Solusi Energi Terbarukan Rumah Tangga,” 2022. [Online]. Available: https://journal.uny.ac.id/index.php/jee
J. Homepage, A. Roihan, P. Abas Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper,” 2019.
T. P. Carvalho, F. A. A. M. N. Soares, R. Vita, R. da P. Francisco, J. P. Basto, and S. G. S. Alcalá, “A Systematic Siterature Review of Machine Learning Methods Applied to Predictive Maintenance,” Comput Ind Eng, vol. 137, Nov. 2019, doi: 10.1016/j.cie.2019.106024.
F. Baharuddin and A. Tjahyanto, “Peningkatan Performa Klasifikasi Machine Learning Melalui Perbandingan Metode Machine Learning dan Peningkatan Dataset,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 11, no. 1, pp. 25–31, Mar. 2022, doi: 10.32736/sisfokom.v11i1.1337.
H. Rathore, H. K. Meena, and P. Jain, “Prediction of EV Energy consumption Using Random Forest And XGBoost,” in Proceedings - 2nd International Conference on Power Electronics and Energy, ICPEE 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICPEE54198.2023.10060798.
Ardianto, A. Raharjo, and D. Purwitasari, “Random Forest Regression Untuk Prediksi Produksi Daya Pembangkit Listrik Tenaga Surya,” BRILIANT: Jurnal Riset dan Konseptual, vol. 7, pp. 1058–1075, 2022, doi: http://dx.doi.org/10.28926/briliant.v7i4. 1036.
H. Gufron, D. Rusirawan, and L. Widyawati, “Forecasting Produksi Energi PLTS 1 kWp Menggunakan Mesin Pembelajaran Dengan Algoritma Support Vector Machine,” Jurnal Tekno Insentif, vol. 16, no. 2, pp. 79–91, Dec. 2022, doi: 10.36787/jti.v16i2.843.
M. M. Gokce and E. Duman, “Performance Comparison of Simple Regression, Random Forest and XGBoost Algorithms for Forecasting Electricity Demand,” in 3rd International Informatics and Software Engineering Conference, IISEC 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/IISEC56263.2022.9998213.
D. Liu and K. Sun, “Random forest solar power forecast based on classification optimization,” Energy, vol. 187, Nov. 2019, doi: 10.1016/j.energy.2019.115940.
Y. A. Singgalen, “Penerapan Metode CRISP-DM dalam Klasifikasi Data Ulasan Pengunjung Destinasi Danau Toba Menggunakan Algoritma Naïve Bayes Classifier (NBC) dan Decision Tree (DT),” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 7, pp. 1551–1526, 2023, doi: 10.30865/mib.v7i3.6461.
C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 526–534. doi: 10.1016/j.procs.2021.01.199.
S. Hani, G. Santoso, S. Subandi, and N. Arifin, “Perencanaan Pembangkit Listrik Tenaga Surya (PLTS) On-Grid Dengan Sistem DC Coupling Berkapasitas 17 kWP Pada Gedung,” Prosiding Seminar Nasional Teknoka, vol. 5, pp. 156–163, Dec. 2020, doi: 10.22236/teknoka.v5i.300.
A. Santosa, S. Hani, and G. Santoso, “Perancangan Sistem PLTS Off-grid Kapasitas 100 Wp Sebagai Sumber Energi Alternatif Charging 220 V di Daerah Terdampak Bencana Semeru,” 2022.
N. Aini, A. Lestari, M. N. Hayati, F. Deny, and T. Amijaya, “Analisis Cluster Pada Data Kategorik dan Numerik dengan Pendekatan Cluster Ensemble (Studi Kasus: Puskesmas di Provinsi Kalimantan Timur Kondisi Desember 2017),” Jurnal EKSPONENSIAL, vol. 11, no. 2, 2020.
B. Julian, Muliadi, and Syukri, “Analisis Pengaruh Radiasi Matahari Dan Temperatur Terhadap Daya Keluaran Fotovoltaik Menggunakan SPSS,” Aceh Journal of Electrical Engineering and Technology, vol. 3, 2023.
H. Johan, N. Utomo, and W. W. Rendy, “Pengaruh Temperatur Udara, Kelembaban Udara, Kecepatan Udara dan Intensitas Cahaya Terhadap Daya Listrik Panel Surya,” 2022.
A. Ferdita Nugraha, Y. Pristyanto, and I. Pratama, “Penanganan Missing Values Untuk Meningkatkan Kinerja Model Machine Learning Pada Data Telemarketing,” 2020. [Online]. Available: www.ejournal.unib.ac.id/index.php/pseudocode
E. Blessing and A. K. Hubert, “Normalization and Standardization: Methods to preprocess data to have consistent scales and distributions,” 2023. [Online]. Available: https://www.researchgate.net/publication/377123133
W. Aprizal, I. Ariawan, A. Armelita, and N. Tufailah, “Data Scaling Performance on Various Machine Learning Algorithms to Identify Abalone Sex,” Jurnal Teknologi dan Sistem Komputer, vol. 10, no. 1, pp. 26–31, 2022, doi: 10.14710/jtsiskom.2022.14105.
R. Bayindir, M. Yesilbudak, M. Colak, and N. Genc, “A novel application of naive bayes classifier in photovoltaic energy prediction,” in Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, Institute of Electrical and Electronics Engineers Inc., 2017, pp. 523–527. doi: 10.1109/ICMLA.2017.0-108.
R. Supriyadi, W. Gata, N. Maulidah, and A. Fauzi, “Penerapan Algoritma Random Forest Untuk Menentukan Kualitas Anggur Merah,” vol. 13, no. 2, pp. 67–75, Dec. 2020, [Online]. Available: http://journal.stekom.ac.id/index.php/E-Bisnispage67
irwansyah saputra and kristiyanti dinar ajeng, Machine Learning Untuk Pemula. bandung: informatika bandung, 2022.
G. Ashfania, achmad Widodo, T. Warsokusumo, and T. Prahasto, “Penggunaan Algoritma Random Forest untuk Klasifikasi berbasis Kinerja Efisiensi Energi pada Sistem Pembangkit Daya,” jakarta, Jul. 2023.
D. Suryani, A. Labellapansa, M. L.Shiddiqie, and A. Hidayat, “Kajian Klasifikasi Data Mining IQ Siswa SMA Berdasarkan Hasil Intelligence Structure Test Dengan Menggunakan Metode Naive Bayes,” Seminar Nasional Informatika Medis (SNIMed), 2019.
A. Fahrizal, D. Rusirawan, and L. Lidyawati, “Pemodelan Produksi Energi Pembangkit Listrik Tenaga Surya 1000 Wp Dengan Algoritma Naive Bayes,” Jurnal Tekno Insentif, vol. 16, no. 2, pp. 105–118, Dec. 2022, doi: 10.36787/jti.v16i2.864.
K. Immanuel Rui Costa, “Pengembangan dan Pembuatan Website: Sebuah Tinjauan Literatur,” palangkaraya, Apr. 2022. Accessed: Jul. 07, 2024. [Online]. Available: https://www.researchgate.net/publication/359815377
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