# TRNSYS Simulation and Experimental Validation of Internal Temperature and Heating Demand in a Glass Greenhouse

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

#### Scope of the Study

^{2}was divided in two (farm A, 2160 m

^{2}, and farm B, 1782 m

^{2}) based on their geometry to study the effect of the radiation modes on the internal temperature and heating demand of the greenhouse. The resulting TRNSYS simulation results were validated with the experimental measurements of farm A (concave-shaped) and farm B (convex-shaped) greenhouses. The major findings from this research will assist greenhouse growers, researchers, and engineers to choose the best radiation mode to model the thermal performance of a greenhouse depending on the greenhouse shape.

## 2. Materials and Methods

#### 2.1. Description of the Experimental Site

^{2}in three parts, farm A (2160 m

^{2}), farm B (1782 m

^{2}), and a packaging room (432 m

^{2}), represented by A, B, and C, respectively, in Figure 2. Farms A and B are Venlo-roofed, multi-span, glass greenhouses with dimensions of 32 m × 67.5 m × 7.25 m and 36 m × 49.5 m × 7.25 m, respectively. The vertical view of one of the spans and sensor positions are shown in Figure 3. The total net conditioned volume of the greenhouse was 22,942.4 m

^{3}, with a net conditioned area of 3942 m

^{2}.

^{−1}) at height h (m) of the greenhouse, ${V}_{o}$ is the downloaded KMA station wind speed (ms

^{−1}) at height ${h}_{o}$ (m), and ∝ is the power law exponent. The power law exponent is an empirically derived coefficient that increases in value with increase in terrain roughness. In an experiment conducted by Jung et al. [32] in a coastal region (Buan-gu) similar to the experimental site, the power law exponent was 0.28.

^{−1}), $\dot{m}$ is the mass flow rate (kgh

^{−1}), ${C}_{p}$ is the specific heat capacity of water (kcalkg

^{−1}$\mathrm{K}$

^{−1}), and $\Delta t$ is the difference between inlet and outlet water temperatures ($\mathrm{K}$).

#### 2.2. Greenhouse Material Properties

_{b}is the upward longwave radiation from the material to the sky (Wm

^{−2}), Q

_{c}is the upward longwave radiation from the material to the black fabric during the night (Wm

^{−2}), Q

_{d}is the inward longwave radiation from the black fabric toward the material (Wm

^{−2}), Q

_{a}is the inward sky radiation toward the material (Wm

^{−2}), ρ

_{L}is the longwave reflectance of the material, τ

_{L}is the longwave transmittance of the material, and E

_{s}is the emissive power of the material (Wm

^{−2}) from the Stefan–Boltzmann’s law. E

_{s}is calculated using Equation (6):

_{b}is the outward shortwave radiation from the material toward the sky, S

_{c}is the outward shortwave radiation from the material toward the black material, ρ

_{s}is the reflectance of the material, τ

_{s}is the transmittance of the material, and S

_{a}and S

_{d}are the downward shortwave radiation from the sky and the radiation from the black fabric toward the material, respectively.

#### 2.3. Greenhouse Simulation Modelling in TRNSYS 18

_{i}is the greenhouse’s internal temperature.

^{−1}was used for the equipment gain [37]. However, internal gain due to plant evapotranspiration was not considered.

^{−1}), ${\dot{Q}}_{surf,i}$ is the convective gain from surfaces (kJh

^{−1}), ${\dot{Q}}_{inf,i}$ is the infiltration gains (kJh

^{−1}), ${\dot{Q}}_{ven,i}$ is the ventilation gains (kJh

^{−1}), ${\dot{Q}}_{g,c,i}$ is the internal convective gains (kJh

^{−1}), ${\dot{Q}}_{cplg,i}$ is the gains due to inter-connected air nodes (kJh

^{−1}), ${\dot{Q}}_{solar,i}$ is the solar radiation entering an air node through external windows, transformed immediately into a convective gain to the internal air (kJh

^{−1}), and ${\dot{Q}}_{ISHCC,i}$ is the solar radiation absorbed on all internal shading devices of the zone transformed immediately into a convective gain to the internal air (kJh

^{−1}).

^{−1}), ${h}_{v}$ is the heat of vaporisation of water (kJkg

^{−1}), ${\dot{m}}_{inf}$ is the mass flow rate of infiltration air (kgm

^{−3}), ${w}_{a}$ is the ambient humidity ratio (${\mathrm{kg}}_{water}{\mathrm{kg}}_{air}{}^{-1}$), ${w}_{i}$ is the air node humidity ratio (${\mathrm{kg}}_{water}{\mathrm{kg}}_{air}{}^{-1}$), ${\dot{m}}_{vent}$ is the mass flow rate of ventilation air (kg${\mathrm{m}}^{-3}$), ${w}_{vent}$ is the humidity ratio of ventilation air (${\mathrm{kg}}_{water}{\mathrm{kg}}_{air}{}^{-1}$), ${W}_{g}$ is the internal humidity gain (${\mathrm{kg}}_{water}{\mathrm{h}}^{-1}$), ${\dot{m}}_{ig}$ is the mass flow rate due to couplings of two zones (kg${\mathrm{m}}^{-3}$), ${w}_{j}$ is the adjacent air node humidity ratio (${\mathrm{kg}}_{water}{\mathrm{kg}}_{air}{}^{-1}$), ${M}_{eff}$ is the effective moisture capacitance (kg), and $\Delta t$ is the change in time step.

^{−1}), $1\to 2$ is the flow direction from one air node to another, ${C}_{d}$ is the discharge coefficient, H is the total height of the window (m), $\rho \left(z\right)$ is the air density (kgm

^{−3}) at height z, z is the height of the opening (m), $\alpha $ is the angle of opening ($\xb0$), W is the width of rectangular opening (m), and f(z) is the pressure difference at height z (pa).

^{−1}), ${\dot{Q}}_{conv,in}$ is the convective heat flux between the zone and the inner surface (kJ${\mathrm{h}}^{-1}$), ${\dot{Q}}_{lw,in}$ is the longwave radiation exchange between two inner surfaces (kJ${\mathrm{h}}^{-1}$), ${\dot{Q}}_{solg,rad}$ and ${\dot{Q}}_{ig,rad}$ are the radiative solar and internal gains, respectively (kJ${\mathrm{h}}^{-1}$), ${\dot{Q}}_{conv,out}$ is the convective heat flux between the external surface and ambient (kJ${\mathrm{h}}^{-1}$), ${\dot{Q}}_{solx}$ is the absorbed solar gain on the outside opaque surfaces (kJ${\mathrm{h}}^{-1}$), ${\dot{Q}}_{lw,out}$ is the longwave radiation emitted by the outside surfaces to their surroundings (kJ${\mathrm{h}}^{-1}$), and ${\dot{Q}}_{cond}$ is the heat conduction through the building envelope (kJ${\mathrm{h}}^{-1}$).

#### 2.3.1. Beam Radiation Distribution

#### 2.3.2. Diffuse Radiation Distribution

#### 2.3.3. Longwave Radiation Distribution

_{star}) to consider simultaneously the energy flow from a wall surface by convection to the air node and radiation to other surfaces. A mathematical description of the internal surface is given in Equations (15)–(17) [40]:

^{2}), and ${T}_{s,i}$ and ${T}_{i}$ are the surface and the equivalent air node temperatures (°C), respectively.

#### 2.4. BES Model Validation

#### 2.5. Sensitivity Analysis

## 3. Results and Discussion

#### 3.1. Radiometric Properties of the Novel Greenhouse Materials

#### 3.2. Comparison of Results of the TRNSYS Model with the Experimental Measurements

^{2}and 120.7 kcal/hm

^{2}, respectively, on 26 December when the ambient temperature (−17.8 °C) was minimum and the greenhouse set point temperature was 15 °C. For the same greenhouse with similar hybrid heat pump systems using geothermal sources and solar heat, Jeon et al. [43] designed a greenhouse (1015 m

^{2}) with a maximum heating load of 148.8 kcal/hm

^{2}for the lowest ambient temperature of −19 °C and greenhouse set point temperature of 23 °C. The simulated maximum heating demands for both the farms with the thermal screens were high compared with Rasheed et al. [23], who obtained 109.9 kcal/hm

^{2}, 98 kcal/hm

^{2}, and 81.3 kcal/hm

^{2}using single-, double-, and triple layered-thermal screens, respectively, for a greenhouse floor area of 7572.6 m

^{2}. However, the simulated heating demand was lower than that of a greenhouse with a floor area of 391.2 m

^{2}and with a maximum heating load of 250 kcal/hm

^{2}[44]. This trend indicates that greenhouse heating demand per square meter decreases with increasing floor area. Heat is lost through all greenhouse surfaces, including walls, roofs, and floors, and the amount of heat lost per square meter increases as the wall-to-total surface area ratio increases.

^{2}. This is approximately ten times lower than that of the experimental glass greenhouse. The calculated NSE values for farms A and B were 0.89 and 0.9, respectively, indicating the model’s ability to predict the greenhouse energy load.

#### 3.3. Limitation of the Study and Future Work

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Nomenclature

Symbols | |

${A}_{s,i}$ | Inside surface area, m^{2} |

${C}_{m}$ | Thermal capacitance of the zone masses, kJ${\mathrm{K}}^{-1}$ |

${C}_{p}$ | Specific heat capacity of water, kcalkg^{−1}$\mathrm{K}$^{−1} |

E_{s} | Emissive power of the material, Wm^{−2} |

${f}_{s,sky}$ | View factor of the sky |

${G}_{ir}$ | Gebhart factor |

${G}_{ir}{}^{*}$ | Auxiliary matrix |

${G}_{ir}{}^{T}$ | Transpose of ${G}_{ir}$ |

$h$ | Greenhouse height, m |

${h}_{conv,s,o}$ | Convective heat transfer coefficient at the outside surface, Wm^{−2}K^{−1} |

${h}_{o}$ | Reference height, m |

${h}_{v}$ | Heat of vaporisation of water, kJ${\mathrm{kg}}^{-1}$ |

$\dot{m}$ | Mass flow rate of water, kgh^{−1} |

${\dot{m}}_{ig}$ | Mass flow rate due to couplings of two zones, kg${\mathrm{m}}^{-3}$ |

${\dot{m}}_{inf}$ | Mass flow rate of infiltration air, kg${\mathrm{m}}^{-3}$ |

${\dot{m}}_{vent}$ | Mass flow rate of ventilation air, kg${\mathrm{m}}^{-3}$ |

${M}_{eff}$ | Effective moisture capacitance, kg |

$\dot{{q}_{comb,s,i}}$ | Combined convective and radiative heat flux in the inner surface, kJ${\mathrm{h}}^{-1}$ |

$\dot{{q}_{comb,s,o}}$ | Combined convective and radiative heat flux to the outside surface, kJ${\mathrm{h}}^{-1}$ |

$\dot{{q}_{c,s,o}}$ | Convective heat flux to the outside surface, kJ${\mathrm{h}}^{-1}$ |

$\dot{{q}_{r,s,o}}$ | Radiative heat flux to the outside surface, kJ${\mathrm{h}}^{-1}$ |

Q | Energy consumed, kcalh^{−1} |

Q_{a} | Inward sky radiation toward the material, Wm^{−2} |

Q_{b} | Upward longwave radiation from the material to the sky, Wm^{−2} |

Q_{c} | Upward longwave radiation from the material to the black fabric during the night, Wm^{−2} |

${\dot{Q}}_{cond}$ | Heat conduction through the building envelope, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{conv,in}$ | Convective heat flux between the zone and the inner surface, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{conv,out}$ | Convective heat flux between the external surface and ambient, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{cplg,i}$ | Gains due to inter-connected air nodes kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{g,c,i}$ | Internal convective gains, kJ ${\mathrm{h}}^{-1}$ |

Q_{d} | Inward longwave radiation from the black fabric toward the material, Wm^{−2} |

${\dot{Q}}_{ig,rad}$ | Radiative heat flux, kJ ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{inf,i}$ | Infiltration heat flux, kJ ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{ISHCC,i}$ | Solar radiation absorbed on all internal shading devices of the zone, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{lat,i}$ | Latent energy flux of the zone, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{lw,in}$ | Longwave radiation exchange between two inner surfaces, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{lw,out}$ | Longwave radiation emitted by the outside surfaces to the Surroundings, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{sens,i}$ | Sensible heat flux of the zone, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{solar,i}$ | Solar radiation entering an air node through external windows, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{solx}$ | Absorbed solar gain on the outside opaque surfaces, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{surf,i}$ | Convective gain from surfaces, kJ${\mathrm{h}}^{-1}$ |

${\dot{Q}}_{ven,i}$ | Ventilation heat flux, kJ ${\mathrm{h}}^{-1}$ |

${R}_{star,i}$ | Resistance of each surface, Ohms |

${R}_{equiv,i}$ | Equivalent resistance of all the surfaces, Ohms |

S_{a} | Downward shortwave radiation from the sky, Wm^{−2} |

S_{b} | Outward shortwave radiation from the material toward the sky, Wm^{−2} |

S_{c} | Outward shortwave radiation from the material toward the black material, Wm^{−2} |

S_{d} | Radiation from the black fabric toward the material, Wm^{−2} |

$\Delta t$ | Temperature difference, $\mathrm{K}$ |

$\Delta T$ | Change in simulation time step |

T | Temperature vector of the enclosure |

${T}_{a,s}$ | Outside surface temperature, $\mathrm{K}$ |

${T}_{b}$ | Surface temperature, $\mathrm{K}$ |

${T}_{fsky}$ | Fictive temperature difference between the ground and sky, $\mathrm{K}$ |

${T}_{i}$ | Equivalent air node temperatures, $\mathrm{K}$ |

${T}_{m}$ | Temperature of the zone masses, $\mathrm{K}$ |

${T}_{sgrd}$ | Fictive ground temperature, $\mathrm{K}$ |

${T}_{sky}$ | Fictive sky temperature, $\mathrm{K}$ |

${T}_{s,i}$ | Surface air node temperatures, $\mathrm{K}$ |

${T}_{s,o}$ | Ambient temperature, $\mathrm{K}$ |

T_{star} | Artificial temperature of the air node, $\mathrm{K}$ |

${V}_{h}$ | Calculated wind speed, ms^{−1} |

${V}_{o}$ | Reference wind speed, ms^{−1} |

${w}_{a}$ | Ambient humidity ratio, ${\mathrm{kg}}_{water}{\mathrm{kg}}_{air}{}^{-1}$ |

${w}_{j}$ | Adjacent air node humidity ratio, ${\mathrm{kg}}_{water}{\mathrm{kg}}_{air}{}^{-1}$ |

${w}_{i}$ | Air node humidity ratio, ${\mathrm{kg}}_{water}{\mathrm{kg}}_{air}{}^{-1}$ |

${w}_{vent}$ | Humidity ratio of ventilation air ${\mathrm{kg}}_{water}{\mathrm{kg}}_{air}{}^{-1}$ |

${W}_{g}$ | Internal humidity gain, ${\mathrm{kg}}_{water}{\mathrm{h}}^{-1}$ |

${X}_{i}{}^{exp}$ | Experimentally measured data |

${X}_{i}{}^{sim}$ | Simulated data and |

${X}_{i}{}^{mean}$ | Mean of the experimentally measured data |

Greek symbols | |

∝ | Power law exponent |

ρ_{L} | Longwave reflectance of the material |

τ_{L} | Longwave transmittance of the material |

$\sigma $ | Stefan–Boltzmann constant |

${\epsilon}_{b}$ | Emissivity of the black fabric |

ρ_{s} | Reflectance of the material |

τ_{S} | Transmittance of the material |

${\epsilon}_{s,o}$ | Longwave emissivity of the outside surface from the WINDOW library |

${\rho}_{ir}$ | Diagonal matrices describing reflectivity |

${\epsilon}_{ir}$ | Diagonal matrices describing emissivity |

Abbreviations | |

A | Diagonal matrix describing the surface areas |

I | Identity matrix |

F | View factor |

ir | Longwave range of the radiation spectrum (infrared) |

BES | Building Energy Simulations |

TRNSYS | Transient System Simulation |

NSE | Nash–Sutcliffe Efficiency Coefficient |

CSG | Chinese-style Solar Greenhouse |

HG | Horticultural Glass |

KMA | Korean Meteorological Administration |

QTM | Quick Thermal Meter |

RBM | Radiation Balance Method |

SC | Sensitivity Coefficient |

OP | Output |

IP | Input |

## References

- Baudoin, W.; Nono-Womdim, R.; Lutaladio, N.; Hodder, A.; Castilla, N.; Leonardi, C.; De Pascale, S.; Qaryouti, M. Cultural Practices and Environment. In Hobby Hydroponics; CRC Press: Boca Raton, FL, USA, 2013; pp. 42–55. ISBN 9789251076491. [Google Scholar]
- Akpenpuun, T.D.; Na, W.H.; Ogunlowo, Q.O.; Rabiu, A.; Adesanya, M.A.; Addae, K.S.; Kim, H.T.; Lee, H.-W. Effect of Glazing Configuration as an Energy-Saving Strategy in Naturally Ventilated Greenhouses for Strawberry (Seolhyang Sp.) Cultivation. J. Agric. Eng.
**2021**, 52, 1177. [Google Scholar] [CrossRef] - Ogunlowo, Q.O.; Akpenpuun, T.D.; Na, W.H.; Rabiu, A.; Adesanya, M.A.; Addae, K.S.; Kim, H.T.; Lee, H.W. Analysis of Heat and Mass Distribution in a Single-and Multi-Span Greenhouse Microclimate. Agriculture
**2021**, 11, 891. [Google Scholar] [CrossRef] - Banakar, A.; Montazeri, M.; Ghobadian, B.; Pasdarshahri, H.; Kamrani, F. Energy Analysis and Assessing Heating and Cooling Demands of Closed Greenhouse in Iran. Therm. Sci. Eng. Prog.
**2021**, 25, 101042. [Google Scholar] [CrossRef] - Mazzeo, D.; Baglivo, C.; Panico, S.; Congedo, P.M. Solar Greenhouses: Climates, Glass Selection, and Plant Well-Being. Sol. Energy
**2021**, 230, 222–241. [Google Scholar] [CrossRef] - Akpenpuun, T.D.; Na, W.H.; Ogunlowo, Q.O.; Rabiu, A.; Adesanya, M.A.; Addae, K.S.; Kim, H.T.; Lee, H.W. Effect of Greenhouse Cladding Materials and Thermal Screen Configuration on Heating Energy and Strawberry (Fragaria Ananassa Var. “Seolhyang”) Yield in Winter. Agronomy
**2021**, 11, 2498. [Google Scholar] [CrossRef] - Baglivo, C.; Mazzeo, D.; Panico, S.; Bonuso, S.; Matera, N.; Congedo, P.M.; Oliveti, G. Complete Greenhouse Dynamic Simulation Tool to Assess the Crop Thermal Well-Being and Energy Needs. Appl. Therm. Eng.
**2020**, 179, 115698. [Google Scholar] [CrossRef] - Zhang, Y.; Gauthier, L.; De Halleux, D.; Dansereau, B.; Gosselin, A. Effect of Covering Materials on Energy Consumption and Greenhouse Microclimate. Agric. For. Meteorol.
**1996**, 82, 227–244. [Google Scholar] [CrossRef] - Rasheed, A.; Na, W.H.; Lee, J.W.; Kim, H.T.; Lee, H.W. Optimization of Greenhouse Thermal Screens for Maximized Energy Conservation. Energies
**2019**, 12, 3592. [Google Scholar] [CrossRef] [Green Version] - Shukla, A.; Tiwari, G.N.; Sodha, M.S. Thermal Modeling for Greenhouse Heating by Using Thermal Curtain and an Earth-Air Heat Exchanger. Build. Environ.
**2006**, 41, 843–850. [Google Scholar] [CrossRef] - Ahamed, M.S.; Guo, H.; Tanino, K. Energy Saving Techniques for Reducing the Heating Cost of Conventional Greenhouses. Biosyst. Eng.
**2019**, 178, 9–33. [Google Scholar] [CrossRef] - Rasheed, A.; Lee, J.W.; Lee, H.W. Development of a Model to Calculate the Overall Heat Transfer Coefficient of Greenhouse Covers. Spanish J. Agric. Res.
**2017**, 15, e0208. [Google Scholar] [CrossRef] [Green Version] - Guo, Y.; Zhao, H.; Zhang, S.; Wang, Y.; Chow, D. Modeling and Optimization of Environment in Agricultural Greenhouses for Improving Cleaner and Sustainable Crop Production. J. Clean. Prod.
**2021**, 285, 124843. [Google Scholar] [CrossRef] - Lee, S.Y.; Lee, I.B.; Lee, S.N.; Yeo, U.H.; Kim, J.G.; Kim, R.W.; Decano-Valentin, C. Dynamic Energy Exchange Modelling for a Plastic-Covered Multi-Span Greenhouse Utilizing a Thermal Effluent from Power Plant. Agronomy
**2021**, 11, 1461. [Google Scholar] [CrossRef] - Zhang, G.; Ding, X.; Li, T.; Pu, W.; Lou, W.; Hou, J. Dynamic Energy Balance Model of a Glass Greenhouse: An Experimental Validation and Solar Energy Analysis. Energy
**2020**, 198, 117281. [Google Scholar] [CrossRef] - Baneshi, M.; Gonome, H.; Maruyama, S. Wide-Range Spectral Measurement of Radiative Properties of Commercial Greenhouse Covering Plastics and Their Impacts into the Energy Management in a Greenhouse. Energy
**2020**, 210, 118535. [Google Scholar] [CrossRef] - Ahamed, M.S.; Guo, H.; Tanino, K. A Quasi-Steady State Model for Predicting the Heating Requirements of Conventional Greenhouses in Cold Regions. Inf. Process. Agric.
**2018**, 5, 33–46. [Google Scholar] [CrossRef] - Katzin, D.; van Henten, E.J.; van Mourik, S. Process-Based Greenhouse Climate Models: Genealogy, Current Status, and Future Directions. Agric. Syst.
**2022**, 198, 103388. [Google Scholar] [CrossRef] - Fabrizio, E. Energy Reduction Measures in Agricultural Greenhouses Heating: Envelope, Systems and Solar Energy Collection. Energy Build.
**2012**, 53, 57–63. [Google Scholar] [CrossRef] - Ahamed, M.S.; Guo, H.; Tanino, K. Development of a Thermal Model for Simulation of Supplemental Heating Requirements in Chinese-Style Solar Greenhouses. Comput. Electron. Agric.
**2018**, 150, 235–244. [Google Scholar] [CrossRef] - Mashonjowa, E.; Ronsse, F.; Milford, J.R.; Pieters, J.G. Modelling the Thermal Performance of a Naturally Ventilated Greenhouse in Zimbabwe Using a Dynamic Greenhouse Climate Model. Sol. Energy
**2013**, 91, 381–393. [Google Scholar] [CrossRef] - Ahamed, M.S.; Guo, H.; Tanino, K. Modeling Heating Demands in a Chinese-Style Solar Greenhouse Using the Transient Building Energy Simulation Model TRNSYS. J. Build. Eng.
**2020**, 29, 101114. [Google Scholar] [CrossRef] - Rasheed, A.; Kwak, C.S.; Na, W.H.; Lee, J.W.; Kim, H.T.; Lee, H.W. Development of a Building Energy Simulation Model for Control of Multi-Span Greenhouse Microclimate. Agronomy
**2020**, 10, 1236. [Google Scholar] [CrossRef] - Sharma, P.K.; Tiwari, G.N.; Sorayan, V.P.S. Temperature Distribution in Different Zones of the Micro-Climate of a Greenhouse: A Dynamic Model. Energy Convers. Manag.
**1999**, 40, 335–348. [Google Scholar] [CrossRef] - Asa’d, O.; Ugursal, V.I.; Ben-Abdallah, N. Investigation of the Energetic Performance of an Attached Solar Greenhouse through Monitoring and Simulation. Energy Sustain. Dev.
**2019**, 53, 15–29. [Google Scholar] [CrossRef] - Rabiu, A.; Na, W.; Denen, T.; Rasheed, A.; Aderemi, M. ScienceDirect Determination of Overall Heat Transfer Coefficient for Greenhouse Energy-Saving Screen Using Trnsys and Hotbox. Biosyst. Eng.
**2022**, 217, 83–101. [Google Scholar] [CrossRef] - Castellucci, S.; Carlini, M. Modelling and Simulation for Energy Production Parametric Dependence in Greenhouses. Math. Probl. Eng.
**2010**, 2010, 590943. [Google Scholar] [CrossRef] [Green Version] - Choab, N.; Allouhi, A.; El Maakoul, A.; Kousksou, T.; Saadeddine, S.; Jamil, A. Effect of Greenhouse Design Parameters on the Heating and Cooling Requirement of Greenhouses in Moroccan Climatic Conditions. IEEE Access
**2020**, 9, 2986–3003. [Google Scholar] [CrossRef] - Rasheed, A.; Kim, H.T.; Lee, H.W. Modeling-Based Energy Performance Assessment and Validation of Air-To-Water Heat Pump System Integrated with Multi-Span Greenhouse on Cooling Mode. Agronomy
**2022**, 12, 1374. [Google Scholar] [CrossRef] - Yeoju Climate, Weather by Month, Average Temperature (South Korea)—Weather Spark. Available online: https://weatherspark.com/y/142307/Average-Weather-in-Yeoju-South-Korea-Year-Round (accessed on 4 March 2022).
- Weather Data Opening Portal. Available online: https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=36 (accessed on 18 June 2022).
- Jung, S.-H.; Lee, J.-W.; Lee, S.-Y.; Lee, H.-W. Analysis of Wind Velocity Profile for Calculation of Wind Pressure on Greenhouse. Prot. Hortic. Plant Fact.
**2015**, 24, 135–146. [Google Scholar] [CrossRef] - Valera, M.D.; Molina, A.F.; Alvarez, M. Protocolo de Auditoría Energética En Invernaderos Auditoría Energética de Un Invernadero Para Cultivo de Flor Cortada; Instituto para la diversificacion y ahorro de la Energia: Madrid, Spain, 2008; ISBN 9788496680265. [Google Scholar]
- Rafiq, A.; Na, W.H.; Rasheed, A.; Lee, J.W.; Kim, H.T.; Lee, H.W. Measurement of Longwave Radiative Properties of Energy-Saving Greenhouse Screens. J. Agric. Eng.
**2021**, 52. [Google Scholar] [CrossRef] - Nijskens, J.; Deltour, J.; Coutisse, S.; Nisen, A. Radiometric and thermal properties of the new plastic films for greenhouse covering. Acta Hortic.
**1989**, 77, 7–38. [Google Scholar] [CrossRef] - Rasheed, A.; Lee, J.W.; Lee, H.W. Development and Optimization of a Building Energy Simulation Model to Study the Effect of Greenhouse Design Parameters. Energies
**2018**, 11, 2001. [Google Scholar] [CrossRef] [Green Version] - Solar Energy Laboratory. TRNSYS 18 Manual Documentation; University of Wisconsin: Madison, WI, USA, 2018; Volume 5, Available online: http://www.trnsys.com (accessed on 4 March 2022).
- Transsolar. TRNSYS 18 Manual Documentation (TRNFLOW Manual). 2009. Available online: http://www.transsolar.com (accessed on 4 March 2022).
- Lim, A. A Comparative Study between TRNSYS and RC Thermal Models to Simulate a District Thermal Demand. Master’s Thesis, Eindhoven University of Technology, Eindhoven, The Netherlands, 2020. Available online: https://research.tue.nl/en/studentTheses/a-comparative-study-between-trnsys-and-rc-thermal-models-to-simul (accessed on 4 March 2022).
- Seem, J.E. Modeling of Heat in Buildings. Ph.D. Thesis, Solar Energy Laboratory, University of Wisconsin Madison, Madison, WI, USA, 2011. [Google Scholar]
- Lam, J.C.; Hui, S.C.M. Sensitivity Analysis of Energy Performance of Office Buildings. Build. Environ.
**1996**, 31, 27–39. [Google Scholar] [CrossRef] - Choab, N.; Allouhi, A.; El Maakoul, A.; Kousksou, T.; Saadeddine, S.; Jamil, A. Review on Greenhouse Microclimate and Application: Design Parameters, Thermal Modeling and Simulation, Climate Controlling Technologies. Sol. Energy
**2019**, 191, 109–137. [Google Scholar] [CrossRef] - Jeon, J.G.; Lee, D.G.; Paek, Y.; Kim, H.G. Study on Heating Performance of Hybrid Heat Pump System Using Geothermal Source and Solar Heat for Protected Horticulture. J. Korean Sol. Energy Soc.
**2015**, 35, 49–56. [Google Scholar] [CrossRef] [Green Version] - Rasheed, A.; Na, W.H.; Lee, J.W.; Kim, H.T.; Lee, H.W. Development and Validation of Air-to-Water Heat Pump Model for Greenhouse Heating. Energies
**2021**, 14, 4714. [Google Scholar] [CrossRef] - Adjustment, A.H. Long-Term Trend of Surface Wind Speed in Korea: Anemometer Height Adjustment. Atmosphere
**2021**, 31, 101–112. [Google Scholar] - Kim, M.H.; Kim, D.; Heo, J.; Lee, D.W. Techno-Economic Analysis of Hybrid Renewable Energy System with Solar District Heating for Net Zero Energy Community. Energy
**2019**, 187, 115916. [Google Scholar] [CrossRef] - Cooper, P.I.; Fuller, R.J. A Transient Model of the Interaction between Crop, Environment and Greenhouse Structure for Predicting Crop Yield and Energy Consumption. J. Agric. Eng. Res.
**1983**, 28, 401–417. [Google Scholar] [CrossRef]

**Figure 6.**Schematic diagram of the incoming (Q

_{a}and Q

_{d}) and outgoing (Q

_{b}and Q

_{c}) longwave radiations for the greenhouse materials during the night [26].

**Figure 7.**Schematic diagram of the inward (S

_{a}and S

_{d}) and outward (S

_{b}and S

_{c}) shortwave radiations for the greenhouse materials during the daytime [26].

**Figure 8.**Experimental setup for determining the radiometric properties of the greenhouse materials.

**Figure 17.**Comparison of greenhouse zone 1 temperature from experiment and simulation: (

**a**) Farm A; (

**b**) Farm B.

**Figure 18.**Comparison of greenhouse zone 2 temperature from experiment and simulation: (

**a**) Farm A; (

**b**) Farm B.

**Figure 19.**Comparison of greenhouse zone 3 temperature from experiment and simulation: (

**a**) Farm A; (

**b**) Farm B.

**Figure 20.**Comparison of greenhouse hourly heating demand from experiment and simulation: (

**a**) Farm A; (

**b**) Farm B.

**Figure 22.**Sensitivity coefficients of TRNSYS radiation modes for the greenhouse heating requirement: (

**a**) Farm A; (

**b**) Farm B.

Parameter | Measurement Method | Sensor | Precision of Sensor | Characteristics |
---|---|---|---|---|

Ambient temperature (°C) | 9 places at 1.92 m above the ground (zone 1) and 1 place at the centre of zone 2 and 3 | HOBO MX1102A | ±0.5% | Field recorded |

Relative humidity (%) | 9 places at 1.92 m above the ground (zone 1) and 1 place at the centre of zone 2 and 3 | HOBO MX1102A | ±0.5% | Field recorded |

Solar radiation (Wm^{−2}) | 7.25 m above the ground (outside the greenhouse) | CMP3 pyranometer | ±2% | Field recorded |

Water temperature (°C) | 1 m above the ground (zone 1) | I-Sensor, PT 100 | ±0.3 °C | Field recorded |

Flow rate (LPM) | 1 m above the ground (zone 1) | KFCM-1000 K-2101083-2 | ±5% | Field recorded |

Wind speed (ms^{−1}) | 10 m above the ground (at the weather station) | Clima sensor, US, Thies Clima | ±5% | KMA |

Wind direction (degree) | 10 m above the ground (at the weather station) | Clima sensor, US, Thies Clima | ±5% | KMA |

Ambient pressure (hPa) | 10 m above the ground (at the weather station) | PTB-220TS, VAISALA | ±5 hPa | KMA |

Cover Characteristics | Covering Material | Thermal Screens | |
---|---|---|---|

HG | Tempa | Luxous | |

Thickness (mm) | 4 | 0.31 | 0.30 |

Solar transmittance (front) | 0.89 | 0.10 | 0.58 |

Solar transmittance (back) | 0.89 | 0.12 | 0.57 |

Solar reflectance (front) | 0.08 | 0.65 | 0.30 |

Solar reflectance (back) | 0.08 | 0.51 | 0.25 |

Visible radiation transmittance (front) | 0.91 | 0.10 | 0.58 |

Visible radiation transmittance (back) | 0.91 | 0.12 | 0.57 |

Visible radiation reflection (front) | 0.08 | 0.65 | 0.30 |

Visible radiation reflection (back) | 0.08 | 0.51 | 0.25 |

Thermal radiation transmittance | 0.1 | 0.05 | 0.38 |

Thermal radiation emission (front) | 0.90 | 0.20 | 0.44 |

Thermal radiation emission (back) | 0.90 | 0.33 | 0.44 |

Thermal conductivity (Wm^{−1}K^{−1}) | 0.10 | 0.52 | 0.06 |

Infiltration (m^{3}h^{−1}m^{2}) | - | 3.62 | 6.45 |

Materials | Thickness (m) | Thermal Conductivity (kJh^{−1}m^{−1}K^{−1}) | Thermal Capacity (kJkg^{−1}K^{−1}) | Density (kgm^{−3}) | Convective Heat Transfer Coefficient (kJh ^{−1}m^{−2}K^{−1}) | |
---|---|---|---|---|---|---|

Front | Back | |||||

Ground | 0.1000 | 0.97 | 0.75 | 2900 | 11 | 0.001 |

Steel | 0.05 | 54 | 1.8 | 7800 | 11 | 64 |

Cover Characteristics | Fluorine Film | Obscura |
---|---|---|

Thickness (mm) | 0.08 | 0.34 |

Solar transmittance (front) | 0.92 | 0.01 |

Solar transmittance (back) | 0.92 | 0.01 |

Solar reflectance (front) | 0.06 | 0.64 |

Solar reflectance (back) | 0.06 | 0.64 |

Visible radiation transmittance (front) | 0.92 | 0.01 |

Visible radiation transmittance (back) | 0.92 | 0.01 |

Visible radiation reflection (front) | 0.06 | 0.64 |

Visible radiation reflection (back) | 0.06 | 0.64 |

Thermal radiation transmittance | 0.94 | 0.001 |

Thermal radiation emission (front) | 0.02 | 0.045 |

Thermal radiation emission (back) | 0.03 | 0.045 |

Thermal conductivity (Wm^{−1}K^{−1}) | 0.15 | 0.35 |

Infiltration (m^{3}h^{−1}m^{2}) | - | - |

Farm | Greenhouse Set Point Temperature (°C) | Maximum Outside Temperature (°C) | Greenhouse Heating Area (m^{2}) | Radiation Mode | Maximum Heating Load (kcal/hm^{2}) |
---|---|---|---|---|---|

A | 15 | −17.8 | 2160 | Simple | 101.3 |

Standard | 113.5 | ||||

B | 15 | −17.8 | 1782 | Simple | 116.4 |

Standard | 123.4 | ||||

Detailed | 120.7 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Adesanya, M.A.; Na, W.-H.; Rabiu, A.; Ogunlowo, Q.O.; Akpenpuun, T.D.; Rasheed, A.; Yoon, Y.-C.; Lee, H.-W.
TRNSYS Simulation and Experimental Validation of Internal Temperature and Heating Demand in a Glass Greenhouse. *Sustainability* **2022**, *14*, 8283.
https://doi.org/10.3390/su14148283

**AMA Style**

Adesanya MA, Na W-H, Rabiu A, Ogunlowo QO, Akpenpuun TD, Rasheed A, Yoon Y-C, Lee H-W.
TRNSYS Simulation and Experimental Validation of Internal Temperature and Heating Demand in a Glass Greenhouse. *Sustainability*. 2022; 14(14):8283.
https://doi.org/10.3390/su14148283

**Chicago/Turabian Style**

Adesanya, Misbaudeen Aderemi, Wook-Ho Na, Anis Rabiu, Qazeem Opeyemi Ogunlowo, Timothy Denen Akpenpuun, Adnan Rasheed, Yong-Cheol Yoon, and Hyun-Woo Lee.
2022. "TRNSYS Simulation and Experimental Validation of Internal Temperature and Heating Demand in a Glass Greenhouse" *Sustainability* 14, no. 14: 8283.
https://doi.org/10.3390/su14148283