# A Study of the Soil–Wall–Indoor Air Thermal Environment in a Solar Greenhouse

## TL;DR

This study examines how the thermal environment in solar greenhouses is affected by the interaction between soil, walls, and indoor air, using different greenhouse spans and advanced modeling techniques.

## Contribution

The study introduces a novel analysis of the synergistic thermal behavior in solar greenhouses with different spans using K-means, CFD, and LSTM models.

## Key findings

- S1 greenhouse had lower indoor and soil temperatures than S2 when spans and temperatures were the same.
- An isothermal layer was identified in the north wall of both greenhouses with specific horizontal distances from the wall surfaces.
- The CFD and LSTM models effectively predicted and analyzed the thermal environment, aiding in greenhouse optimization.

## Abstract

Greenhouses offer optimal environments for crop cultivation during the winter months. The rationale for this study was identified as the synergistic exchange of air between the soil, the wall, and the indoor environment within the greenhouse (referring to the coupling law of the temperature fields of the three elements in space and time, including the direction of heat transfer and the consistency of the temperature zoning), thereby maintaining a more optimal temperature. However, there is a paucity of research on the impact of different spans on the thermal environment in solar greenhouses and even fewer studies on the synergistic law of changes in soil-wall indoor air in solar greenhouses with different spans. In this study, two solar greenhouses with different spans were analyzed through a combination of experiments as follows: K-means classification optimized using the grey wolf optimizer (GWO), computational fluid dynamics (CFD) simulations, and long short-term memory (LSTM) prediction models. The two solar greenhouses, designated as S1 and S2, had spans of 11 m and 10 m, respectively. The results are as follows: In two greenhouses when the span and temperature were the same, the indoor air temperature and soil temperature of the S1 greenhouse were lower than those of the S2 greenhouse; there was an isothermal layer in the north wall of greenhouses S1 and S2 (a stable area where the temperature change over time is less than 0.5 °C), the horizontal distance between the isothermal layer on the inside of the greenhouse wall and the inside of the wall was more than 400 mm, and that of the outside of the greenhouse wall was more than 200 mm; within the solar greenhouse, this study identified that heat was emitted from the inner surface of the wall (at 0 mm from the inner surface) toward the outer surface of the wall (at 0 mm from the outer surface), as well as at a horizontal distance of 200 mm from the inner surface of the wall. The temperature data from 0:00 to 8:00 at night were selected for the purpose of analyzing the temperature synergistic change in soil-wall indoor air in the S1 greenhouse. The temperature change can be classified into four categories according to K-means classification, which was optimized based on the grey wolf algorithm. The categories were as follows: high-temperature region, medium-high temperature region, medium-low temperature region, and low-temperature region. The low-temperature region spanned the range of X = (800, 3000) mm, and its height range was Y = (−150, 1200) mm. The CFD model and LSTM prediction model have been shown to be superior, and the findings of this study offer a theoretical basis for the optimization of thermal environment control in solar greenhouses.