Sezgisel Algoritma Tabanlı Yapay Sinir Ağları Kullanılarak Fotovoltaik Panel Güç Çıkışlarının Tahmini ve Karşılaştırılması

Author:

DANDIL Emre,GÜRGEN Erol

Publisher

European Journal of Science and Technology

Reference22 articles.

1. 1. Theodoropoulos, K., et al. (2017). Monthly Electricity Statistics. International Energy Agency, (https://www.iea.org/media/statistics/surveys/electricity/mes.pdf).

2. 2. Engin, S., Gülersoy, t. (2018). Hibrid Güç Sistemleri İçin Evirici Tasarımı, Avrupa Bilim ve Teknoloji Dergisi, 14, pp. 228-234.

3. 3. Parmaksiz, H., Karafil A., Özbay H., Kesler M. (2016). Farklı Eğim Açılarındaki Fotovoltaik Panellerin Elektriksel Ölçümlerinin Raspberry Pi ile İzlenmesi, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 4(2),pp. 711-718.

4. 4. Lorenz, E., Hurka, J., Heinemann, D., et al. (2009). Irradiance forecasting for the power prediction of grid-connected photovoltaic systems, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(1),pp. 2-10.

5. 5. Kudo, M., Nozaki, Y., Endo, H. (2009). Forecasting electric power generation in a photovoltaic power system for an energy network. Electrical Engineering in Japan, 167(4), pp. 16-23.

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