Solar air heater with underground latent heat storage system for greenhouse heating: Performance analysis and machine learning prediction

Abstract

The increasing demand for renewable energy sources in greenhouse heating, driven by the high cost of fossil fuels, has prompted the exploration of various alternatives, such as solar collectors, heat pumps, biomass, and cogeneration systems. This study aimed to establish an optimal environment for plant growth by employing a unique solar air heater and an underground latent heat storage system with a packed bed of phase change material unit (CaCl2-6H2O). Conducted in a double-span greenhouse in Ghardaia, Algeria, characterized by a semi-arid climate, the research utilized two distinct machine learning algorithms to predict the heating system's thermal behavior accurately. An experimental assessment of climatic parameters revealed that the greenhouse equipped with the heating system maintained an air temperature 57 % higher than that of a conventional greenhouse during the nighttime. The use of phase change materials resulted in the release of only 20 kJ of energy at night, indicating the potential to meet 30 % of the greenhouse's energy requirements during nighttime. Utilizing artificial neural networks, this study accurately predicted internal greenhouse parameters with and without LTES. The Nonlinear Autoregressive Exogenous (NARX) model exhibited high accuracy in prediction, with an R2 value of 0.9986 in both cases, while the Recurrent Neural Network (RNN) model showed acceptable performance, achieving an R2 value of 0.9893. These results underscore the potential of ANN models in advancing thermal energy storage technologies and their applicability in sustainable agriculture. This research significantly contributes to thermal energy storage systems and their benefits for sustainable agriculture.