Estimasi Daya Keluaran Fotovoltaik Menggunakan Algortima Genetik
Keywords:
photovoltaik, Output power, Genetic algorithm, MATLAB, Renewable EnergyAbstract
The development of renewable energy systems is a major focus in efforts to reduce environmental impacts and dependence on fossil energy sources. One promising renewable energy source is solar energy, which can be utilized through photovoltaic technology. However, to increase the efficiency of the photovoltaic system, an accurate estimate of the output power produced is required. This study aims to develop a photovoltaic output power estimation model using MATLAB-based Genetic Algorithm (GA). Genetic Algorithm was chosen because of its ability to find optimal solutions to a problem using the principles of natural selection. In this study, parameters that affect the performance of the photovoltaic system, such as sunlight intensity, cell temperature, and panel tilt angle, will be analyzed and modeled. The results of this photovoltaic output power estimation model are expected to provide more accurate predictions, so that they can be used for decision making in the planning and development of solar energy systems. Through simulation and data analysis, this study is expected to make a significant contribution to the development of photovoltaic technology in Indonesia and increase the efficiency of its use. Keyword : Photovoltaik, Output power, Genetic Algorithm, MATLAB, Renewable EnergyReferences
Y. G. Hegazy, S. M. Tag Eldin, dan E. F. El-Saadany, “Optimasi kinerja sistem PV menggunakan algoritma genetika,” Energy Reports, vol. 2, hlm. 221–228, 2019.
M. Z. Saharudin, A. B. M. Sharif, dan M. A. M. Radzi, “Pengaruh kondisi lingkungan terhadap kinerja sistem fotovoltaik: Tinjauan,” Jurnal Kejuruteraan Elektrik dan Tenaga, vol. 10, no. 2, hlm. 22–29, 2020.
Sutanto, D., & Pramono, S. H. (2018). Optimasi Keluaran Energi Panel Surya Menggunakan Metode Algoritma Genetika. Jurnal Teknik Elektro dan Komputer, 7(2), 102–108.
Guzman Razo, D. E., Müller, B., Madsen, H., & Wittwer, C. (2020). Pendekatan Algoritma Genetika sebagai Alat Pembelajaran Mandiri dan Optimasi untuk Simulasi Daya PV dan Pembuatan Kembar Digital. Energies, 13(24), 6712.
Alayi, R., Harasii, H., & Pourderogar, H. (2021). Pemodelan dan Optimasi Sel Fotovoltaik dengan Algoritma Genetika. Journal of Robotics and Control (JRC), 2(4), 181–186.
Irwanto, M., Badlishah, R., Masri, M., Irwan, Y. M., & Gomesh, N. (2014). Optimasi karakteristik listrik modul fotovoltaik menggunakan algoritma (hlm. 532–536). IEEE. https://doi.org/10.1109/PEOCO.2014.6814486
Riandra, J. (2020). Analisis dan implementasi algoritma genetika untuk estimasi daya keluaran PLTH fotovoltaik dan diesel.
Waimbo, K. (2024). Pemodelan Modul Fotovoltaik Sederhana Menggunakan MATLAB/Simulink. Jurnal Fisika Papua, 3(1), 1–7.
Alayi, R., Harasii, H., & Pourderogar, H. (2021). Pemodelan dan optimasi sel fotovoltaik dengan algoritma genetika. Journal of Robotics and Control (JRC), 2(4), 181–186.
Gunasekaran, V., Kovi, K. K., Arja, S., & Chimata, R. (2021). Peramalan radiasi matahari menggunakan algoritma genetika. arXiv preprint. https://arxiv.org/abs/2106.13956
A. H. Alami, A. H. Salim, dan M. J. Khan, “Pengaruh temperatur dan iradiasi terhadap kinerja modul fotovoltaik,” Energy Reports, vol. 7, hlm. 3856–3864, 2021. Tersedia secara daring: https://doi.org/10.1016/j.egyr.2021.06.081
A. Mellit dan S. A. Kalogirou, “Teknik kecerdasan buatan untuk aplikasi fotovoltaik: Sebuah tinjauan,” Progress in Energy and Combustion Science, vol. 34, no. 5, hlm. 574–632, 2018.
L. Wang, C. Wang, dan Y. Wang, “Prediksi keluaran daya PV menggunakan jaringan saraf tiruan yang dioptimalkan dengan algoritma genetika,” Renewable Energy, vol. 146, hlm. 2033–2045, 2020.
M. Esfahanian dan B. Vahidi, “Meningkatkan kinerja sistem PV menggunakan estimasi parameter berbasis GA,” IEEE Transactions on Sustainable Energy, vol. 10, no. 3, hlm. 1205–12132019
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