Volume 21, Issue 55, January - June, 2024

Enhancing predictive accuracy of inside temperature and humidity in an agricultural greenhouse using data-driven modeling with artificial neural networks

Djemoui Lalmi^1♦, Kamel Bouaraour¹, Abdelouahab Benseddik³, Ahmed Badji², Hocine Bensaha³, Abdeslem Kifouche¹, Khadidja Khodja⁴

¹Laboratory of Materials, Energy Systems Technology and Environment, Faculté des Science et Technologie, Département d'automatique et électromécanique Université de Ghardaia, Ghardaia, Algeria
²Laboratoire d’Instrumentation, Faculté de Génie Electrique, Université des Sciences et de la Technologie Houari Boumediene, BP 32, El-Alia, 16111 Bab-Ezzouar, Alger, Algeria
³Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Algeria
⁴Centre de Développement des Energies Renouvelables, CDER, Algeria

^♦Corresponding author
Laboratory of Materials, Energy Systems Technology and Environment, Faculté des Science et Technologie, Département d'automatique et électromécanique Université de Ghardaia, Ghardaia, Algeria

ABSTRACT

This work presents an original and innovative approach by combining greenhouse cooling with artificial intelligence models to ensure food security. The study is divided into two parts: Experimental and theoretical. In the first part, a cooling system was implemented in a tunnel-type agricultural greenhouse and compared to a control system. The cooling system consists of multiple fans powered by two solar panels. Data was collected using an acquisition system (Arduino) over approximately one month. This data, along with external data, was utilized to predict the internal temperature of the greenhouse using backpropagation neural network models. The results obtained demonstrate the reliability of the model based on all tests, as evidenced by the coefficient of determination (R2) and the mean square error (MSE) for the prediction.

Keywords: Climate, temperature, agricultural greenhouse; cooling system, and neural network

Indian Journal of Engineering, 2024, 21(55), e6ije1681

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DOI: https://doi.org/10.54905/disssi.v21i55.e6ije1681

Published: 07 June 2024