Contribution to the study of the microclimate in greenhouse equipped with thermal storage north wall

Abstract

Protected agriculture faces major climatic challenges, particularly the significant temperature difference between day and night. This requires advanced technological solutions to ensure a suitable indoor climate and enhance productivity. This study aims to develop an integrated system combining efficient design, thermal storage techniques, and artificial intelligence to predict the internal temperature. A solar greenhouse was constructed at the Applied Research Unit for Renewable Energy in Ghardaïa (32.36° North, 3.51° West), incorporating a north wall designed to store solar energy. This wall, with an area of 1.62 m2, was built using locally sourced stones selected for their excellent thermal properties. It stores excess heat during the day and releases it at night, thereby reducing nighttime temperature fluctuations. Experimental measurements showed that the air temperature inside the greenhouse equipped with the thermal wall was about 2.7 °C higher than the outside air temperature at night, demonstrating the system’s effectiveness. This system was coupled with an artificial intelligence model based on artificial neural networks to accurately predict the internal temperature, using indoor and outdoor climatic data collected from the experimental setup. The model showed high performance and good agreement between predicted and measured values, with a correlation coefficient exceeding 98%, confirming its efficiency and reliability as a sustainable and effective solution to the challenges of protected agriculture in harsh environments.

Protected agriculture faces major climatic challenges, particularly the significant temperature difference between day and night. This requires advanced technological solutions to ensure a suitable indoor climate and enhance productivity. This study aims to develop an integrated system combining efficient design, thermal storage techniques, and artificial intelligence to predict the internal temperature. A solar greenhouse was constructed at the Applied Research Unit for Renewable Energy in Ghardaïa (32.36° North, 3.51° West), incorporating a north wall designed to store solar energy. This wall, with an area of 1.62 m2, was built using locally sourced stones selected for their excellent thermal properties. It stores excess heat during the day and releases it at night, thereby reducing nighttime temperature fluctuations. Experimental measurements showed that the air temperature inside the greenhouse equipped with the thermal wall was about 2.7 °C higher than the outside air temperature at night, demonstrating the system’s effectiveness. This system was coupled with an artificial intelligence model based on artificial neural networks to accurately predict the internal temperature, using indoor and outdoor climatic data collected from the experimental setup. The model showed high performance and good agreement between predicted and measured values, with a correlation coefficient exceeding 98%, confirming its efficiency and reliability as a sustainable and effective solution to the challenges of protected agriculture in harsh environments.