Experimental assessment of a greenhouse with and without PCM thermal storage energy and prediction their thermal behavior using machine learning algorithms

This research paper focuses on the design, fabrication, and experimental investigation of a thermal energy storage unit utilizing phase change materials (PCMs) for greenhouses. The study analyzes the performance of PCM heat energy storage systems and uses a machine learning algorithm to forecast greenhouse air temperature. The experimental greenhouse with PCM showed a notable increase in ambient temperature (1-8 degrees C) after midnight compared to conventional greenhouses. The paper provides strategies for implementing PCMs and outlines an operation strategy for achieving near-zero energy consumption in solar greenhouses during winter. The ANN algorithm demonstrated promising results for predicting internal greenhouse parameters. Overall, this study contributes to the advancement of thermal energy storage systems and their potential applications in sustainable agriculture.

Automated summary

This research paper focuses on the design, fabrication, and experimental investigation of a thermal energy storage unit utilizing phase change materials (PCMs) for greenhouses. The study analyzes the performance of PCM heat energy storage systems and uses a machine learning algorithm to forecast greenhouse air temperature. The experimental greenhouse with PCM showed a notable increase in ambient temperature (1-8 degrees C) after midnight compared to conventional greenhouses. The paper provides strategies for implementing PCMs and outlines an operation strategy for achieving near-zero energy consumption in solar greenhouses during winter. The ANN algorithm demonstrated promising results for predicting internal greenhouse parameters. Overall, this study contributes to the advancement of thermal energy storage systems and their potential applications in sustainable agriculture.