Artificial Neural Network Application for the Prediction of Global Solar Radiation Inside a Greenhouse

Author:

Bezari Salah,Adda Asma,Kherrour Sofiane,Zarrit Reda

Publisher

Springer Nature Switzerland

Reference15 articles.

1. Bezari, S., Bekkouche, A., Bensaha, H., & Benchatti, A. (2015). Amelioration of a greenhouse through energy storage system case study: Ghardaia region. In International conference on renewable energy research and applications (pp. 578–582). IEEE.

2. Adda, A., Hanini, S., Bezari, S., Ameur, H., & Maouedj, R. (2020). Managing and control of nanofiltration/reverse osmosis desalination system: Application of artificial neural network. International Journal of Design & Nature and Ecodynamics, 15(6), 843–853.

3. He, Q., Zheng, H., Ma, X., Wang, L., Kong, H., & Zhu, Z. (2022). Artificial intelligence application in a renewable energy-driven desalination system: A critical review. Energy and AI, 7, 100123.

4. Taki, M., Ajabshirchi, Y., Ranjbar, S. F., & Matloobi, M. (2016). Application of neural networks and multiple regression models in greenhouse climate estimation. Agricultural Engineering International: CIGR Journal, 18(3), 29–43.

5. Belouz, K., Nourani, A., Zereg, S., & Bencheikh, A. (2022). Prediction of greenhouse tomato yield using artificial neural networks combined with sensitivity analysis. Scientia Horticulturae, 293, 110666.

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