Simulation of thermal performance of solar greenhouse in north-west of Iran: An experimental validation

Mobtaker, Hassan Ghasemi; Ajabshirchi, Yahya; Ranjbar, Seyed Faramarz; Matloobi, Mansour

doi:10.1016/j.renene.2018.10.003

Cited by 76 publications

(31 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, heat is required during the winter months to guarantee adequate growth and cooling is necessary during the summer months to assure proper mushroom growth. In this production process, the energy cost of the system is directly related to the greenhouse design [26,27] . This requires the shade room structure to have good thermal insulation performance to reduce the operating energy consumption of the system.…”

Section: Design Factor Analysismentioning

confidence: 99%

“…EVA plastic at the innermost layer; the thickness is 0.0001 m. Table 4 Thermal performance parameters of the simulated greenhouse materials [26] Material Model B has the same construction and dimensions as model A except for the addition of the newly implemented shade room roof. Models A and B are equipped with mechanical ventilation openings on the shade room roof and no windows are installed.…”

Section: South Roof Materials and Constructionmentioning

confidence: 99%

See 1 more Smart Citation

Structural design and thermal performance simulation of shade roof of double-slope greenhouse for mushroom-vegetable cultivation

et al. 2019

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

A new type of roof structure was developed for the shade room in a double-slope greenhouse used for mushroom-vegetable planting. A simulation model was developed to evaluate the thermal performance of the new roof with an insulation thickness of 0.12 m in Beijing, China. The results showed that (1) the indoor air temperature of the shade room with the newly implemented shade roof was 2.7°C-4.9°C higher than that of an ordinary shade room during the winter months; (2) The indoor air temperature of the solar room adjacent to the shade room with the new roof was higher than that of the ordinary solar room and the minimum indoor air temperature of the solar room was increased 1.9°C at winter night; (3) the indoor temperature of the shade room with the new roof design was 2°C-4°C lower than that of the ordinary shade room during the summer months; (4) Under factory production conditions, which were conducted in a controlled environment to promote the annual growth of the edible fungus, the heating energy consumption of the shade room after the implementation of the new roof structure was reduced by 69.3%, the amounted to total energy savings of 61.3% per year. The new roof structure provided a significant improvement in the thermal environment compared to an ordinary shade room, improved the vegetable growth in the winter, and also significantly reduced the energy consumption and production costs.

show abstract

Section: Design Factor Analysismentioning

confidence: 99%

Section: South Roof Materials and Constructionmentioning

confidence: 99%

Structural design and thermal performance simulation of shade roof of double-slope greenhouse for mushroom-vegetable cultivation

et al. 2019

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

show abstract

“…However, the internal environment of a greenhouse can be harsh for electrical sensors. The greenhouse may be close to water, and high solar radiation could heat the sensors [ 12 ]. Root-zone sensors could also be blocked by irrigational problems [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate Imputation of Greenhouse Environment Data for Data Integrity Utilizing Two-Dimensional Convolutional Neural Networks

Moon

Lee

Son

2021

Sensors

View full text Add to dashboard Cite

Greenhouses require accurate and reliable data to interpret the microclimate and maximize resource use efficiency. However, greenhouse conditions are harsh for electrical sensors collecting environmental data. Convolutional neural networks (ConvNets) enable complex interpretation by multiplying the input data. The objective of this study was to impute missing tabular data collected from several greenhouses using a ConvNet architecture called U-Net. Various data-loss conditions with errors in individual sensors and in all sensors were assumed. The U-Net with a screen size of 50 exhibited the highest coefficient of determination values and the lowest root-mean-square errors for all environmental factors used in this study. U-Net50 correctly learned the changing patterns of the greenhouse environment from the training dataset. Therefore, the U-Net architecture can be used for the imputation of tabular data in greenhouses if the model is correctly trained. Growers can secure data integrity with imputed data, which could increase crop productivity and quality in greenhouses.

show abstract

“…Furthermore, to decrease the solar energy loss through the transparent components in a conventional greenhouse, the installation of an opaque wall was suggested, which made the design of the conventional greenhouse similar to that of a solar greenhouse. It was found that the north wall's insulation can significantly reduce the heating demand of a single span greenhouse in Iran [5,19].…”

Section: Introductionmentioning

confidence: 99%

Solar Radiation Allocation and Spatial Distribution in Chinese Solar Greenhouses: Model Development and Application

Zhang

Xie

et al. 2020

Energies

View full text Add to dashboard Cite

Solar radiation is the sole energy source for Chinese solar greenhouse agriculture. A favorable light environment is the foundation of a desirable crop growth environment, and it is key in solar greenhouse design. In this study, a mathematical model is established to quantitatively evaluate the solar greenhouse light environment. The model was developed considering the greenhouse shape parameters, materials’ optical properties, and interior solar radiation evolution, including the beam radiation, diffuse radiation, and multi-reflection. The model was validated under different weather conditions, and the results reveal a mean percentage error of 1.67 and 10.30% for clear sunny weather and cloudy weather, respectively, and a determination coefficient of 0.9756. By using this model, the solar radiation allocation in a solar greenhouse was calculated to determine the solar radiation availability for the heat-storage north wall and the entire greenhouse, and the dynamical spatial distribution of the solar radiation was obtained to describe the light environment quality. These allow the optimization of the greenhouse lighting regulation and planting pattern. Moreover, several optimizing measures are derived according to the model for improving the low-light environment near the north wall and maximizing the north wall’s heat storage/release capacity in a solar greenhouse.

show abstract

Simulation of thermal performance of solar greenhouse in north-west of Iran: An experimental validation

Cited by 76 publications

References 40 publications

Structural design and thermal performance simulation of shade roof of double-slope greenhouse for mushroom-vegetable cultivation

Structural design and thermal performance simulation of shade roof of double-slope greenhouse for mushroom-vegetable cultivation

Accurate Imputation of Greenhouse Environment Data for Data Integrity Utilizing Two-Dimensional Convolutional Neural Networks

Solar Radiation Allocation and Spatial Distribution in Chinese Solar Greenhouses: Model Development and Application

Contact Info

Product

Resources

About