INOVASI DALAM ENERGI TERBARUKAN: JARINGAN SYARAF TIRUAN UNTUK MERAMALKAN DAYA FOTOVOLTAIK
DOI:
https://doi.org/10.34151/prosidingsnast.v1i1.4985Keywords:
photovoltaic output power, meteorology, backpropagation neural networkAbstract
Variations in meteorological conditions cause intermittency, voltage spikes, and feedback power flow, impacting the uncertainty of photovoltaic output, which affects the reliability, stability, and scheduling of photovoltaic operations. Optimal prediction of photovoltaic output power is necessary in the planning and operation of power systems. Photovoltaic technology is utilized to generate electrical energy from direct sunlight. Climatic factors such as cloud cover, humidity, and wind speed also contribute to the electrical energy produced by solar modules. This research discusses a model for predicting photovoltaic output power for the next day using the backpropagation neural network method and multi-factor analysis. In this study, there are 2 different input neurons with 10 predetermined network architectures, a learning rate of 0.1, and a minimum target error of 0.001. The best performance prediction results with the smallest Mean Squared Error (MSE) value were obtained using a backpropagation neural network structure from the 5-20-1 model, which is almost close to the actual value. However, further research is needed to improve the prediction results.
References
Anggreni, R., Muliadi, & Adriat, R. (2018). Analisis Pengaruh Tutupan Awan Terhadap Radiasi Matahari di Kota Pontianak. Prisma Fisika, 6(3), 214–219. https://doi.org/10.26418/pf.v6i3.28896
Catur Wijaya, F., Lestanti, S., & Faried Rahmat, M. (2023). Penerapan Metode Jst Backpropagation Pada Peramalan Produksi Pastry Di Hyfresh Blitar. JATI (Jurnal Mahasiswa Teknik Informatika), 7(4), 2629–2635. https://doi.org/10.36040/jati.v7i4.7393
Dahliya, Samsurizal, & Pasra, N. (2021). Efisiensi Panel Surya Kapasitas 100 Wp Akibat Pengaruh Suhu Dan Kecepatan Angin. Jurnal Ilmiah Sutet, 11(2), 71–80. https://doi.org/10.33322/sutet.v11i2.1551
Gu, L., Han, Y., Wang, C., Shu, G., Feng, J., & Wang, C. (2018). Inventory prediction based on backpropagation neural network. NeuroQuantology, 16(6), 664–673. https://doi.org/10.14704/nq.2018.16.6.1608
Hao, S. (2018). Using multifactor inputs BP neural network to make power consumption prediction [Binghamton University]. https://www.proquest.com/openview/88b031c5a6060e8e6a06a1aabc56b1ad/1?pq-origsite=gscholar&cbl=18750
Huang, D., & Wu, Z. (2017). Forecasting outpatient visits using empirical mode decomposition coupled with backpropagation artificial neural networks optimized by particle swarm optimization. PLoS ONE, 12(2), 1–17. https://doi.org/10.1371/journal.pone.0172539
Johan, H., Utomo, N., & Wikrama Wardana, R. (2022). Pengaruh Temperatur Udara, Kelembaban Udara, Kecepatan Udara Dan Intensitas Cahaya Terhadap Daya Listrik Panel Surya. Edu Fisika, 7(1), 56–61.
Khairunnisa. (2020). Prediksi Daya Pembangkit Listrik Pv Satu Hari Ke Depan Untuk Memudahkan Manajemen Energi Pada Sistem Menggunakan Neural Network [Institut Teknologi Sepuluh Nopember]. InRepository.Its.Ac.Id. https://repository.its.ac.id/73445/1/07111850010003-Master_Thesis.pdf
Khandakar, A., Chowdhury, M. E. H., Kazi, M. K., Benhmed, K., Touati, F., Al-Hitmi, M., & Gonzales, A. S. P. (2019). Machine Learning Based Photovoltaics (Pv) Power Prediction Using Different Environmental Parameters Of Qatar. Energies, 12(14). https://doi.org/https://doi.org/10.3390/en12142782
Mahendra, L., Maknunah, J., Herwono, B., Anggraini, Y., & Nisa, K. (2021). Prediksi Daya Keluaran Pv Berbasis Jaringan Saraf Tiruan Pada Pusat Perbelanjaan Tangerang. In L. Mahendra (Ed.), Conference on Innovation and Application of Science and Technology (CIASTECH 2021) (Nomor Ciastech, hal. 335–342). Universitas Widyagama Malang. https://publishing-widyagama.ac.id/ejournal-v2/index.php/ciastech/article/view/3327/1784
NurHidayat, T., Subodro, R., & Sutrisno. (2021). Analisis Output Daya Pada Pembangkit Listrik Tenaga Surya Dengan Kapasitas 10Wp, 20Wp Dan 30Wp. jurnal CRANKSHAFT, 4(2), 9–18. https://doi.org/https://doi.org/10.24176/crankshaft.v4i2.6013
Putri, S. W., Marausna, G., & Prasetiyo, E. E. (2022). Analisis Pengaruh Intensitas Cahaya Matahari Terhadap Daya Keluaran Pada Panel Surya. Teknika STTKD, 8(1), 29–37. https://doi.org/https://doi.org/10.56521/teknika.v8i1
Shuvho, M. B. A., Chowdhury, M. A., Ahmed, S., & Kashem, M. A. (2019). Prediction of solar irradiation and performance evaluation of grid connected solar 80KWp PV plant in Bangladesh. Energy Reports, 5, 714–722. https://doi.org/https://doi.org/10.1016/j.egyr.2019.06.011
Simarmata, N. P. E., Estefani, Y., Bahri, B. S., & Sibarani, S. S. (2023). Penggunaan Energi Bersih Menggunakan Panel Surya Di India. Jurnal Energi Baru dan Terbarukan, 4(3), 274–284. https://doi.org/10.14710/jebt.2023.21518
Utami, S., & Daud, A. (2021). Pengaruh Temperatur Panel Surya Terhadap Efisiensi Panel Surya. Jurnal Teknik Energi, 11(1), 7–10. https://doi.org/10.35313/energi.v11i1.2437
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Bagus Tri Kuncoro
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
