# Develop a Smart Microclimate Control System for Greenhouses through System Dynamics and Machine Learning Techniques

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area and Materials

^{2}. Monitoring items consisted of internal/external temperature, internal/external relative humidity, external insolation, wind speed, and wind direction (Table 1). It is noted that this study adopted IoT datasets for model-construction and evaluation purposes only.

#### 2.2. System Dynamics (SD) for Simulating Greenhouse Environment

#### 2.2.1. Formulation of Greenhouse Internal Relative Humidity

^{3}h), β

_{i,t}is the spray efficiency (%), Water

_{i,t}denotes the amount of spray (kg), Vent

_{i,t}denotes the indoor ventilation (kg/h), and H

_{i,t}(H

_{o,t}) denote the internal (external) absolute humidity (kg/m

^{3}) at t. V

_{GH}denotes the total capacity of the greenhouse (m

^{3}), and D

_{air}denotes the air density (1.2 kg/m

^{3}).

_{i,t}(RH

_{o,t}) denotes the indoor (external) relative humidity (%) at t, esi

_{i,t}(esi

_{o,t}) denotes the indoor (external) saturated vapor pressure (kpa) at t, and P

_{atm}denotes the atmospheric pressure (101 kpa).

_{i,t}(T

_{o,t}) denotes the indoor (external) temperature (°C) at t.

_{i,t}is the ventilation utilization factor at t, A

_{GH}is the ventilation area of the greenhouse (m

^{2}), and WS

_{t}denotes the wind speed (m/h) at t.

_{i,t+1}and H

_{i,t}denote the indoor absolute humidity at t + 1 and t (kg/m

^{3}), respectively.

_{i,t+1}denotes the indoor partial pressure of water vapor (kpa) at t + 1.

_{i,t+1}) at t + 1 could be calculated by Equation (10).

#### 2.2.2. Formulation of Greenhouse Internal Temperature

_{i,t}and h

_{o,t}denote the indoor and external enthalpies (kj/kg) in the air at t, respectively; Vent

_{i,t}denotes the ventilation rate (m

^{3}/h) at t; V

_{GH}denotes the total capacity of the greenhouse (m

^{3}); D

_{air}denotes the air density (1.2 kg/m

^{3}); K

_{in}denotes the indoor coating material’s heat-convection parameter in the air (6.4 W/m

^{2}°C); A

_{w}denotes the area of the coating material (m

^{2}); T

_{s,t}, T

_{i,t}, and T

_{f,t}denote the indoor temperature (°C) of the coating material, the indoor temperature (°C), and the indoor ground temperature (°C) at t, respectively; A

_{f}denotes the total ground area of the greenhouse (m

^{2}); and K

_{f}denotes the indoor ground-to-air heat convection parameter (4.65 W/m

^{2}°C).

_{i,t}denotes the indoor absolute humidity (kg/m

^{3}) at t.

_{o,t}denotes the external temperature (°C) at t, and H

_{o,t}denotes the external absolute humidity (kg/m

^{3}) at t.

_{o,t}denotes the external solar radiation (W/m

^{2}) at t, and K

_{out}denotes the thermal conductivity on the surface of the material (6.3 W/m

^{2}°C).

_{o,t}denotes the external insolation at t (W/m

^{2}), and Rn

_{lon}denotes the atmospheric long-wave radiation (343 W/m

^{2}).

^{−8}Wm

^{−2}K

^{−4}).

_{t}denotes the heat moving away due to spray (kj/h), β

_{i,t}denotes the indoor spray efficiency (%) at t, Water

_{i,t}denotes the indoor spray amount (kg/h) at t, and H

_{fg}denotes the latent heat of water evaporation (2256.6 kj/kg).

_{p}denotes the specific heat of the air (1.0052 kj/kg °C).

_{i,t+1}and T

_{i,t}denote the indoor temperature (°C) at t + 1 and t, respectively.

#### 2.3. Machine Learning for Predicting Greenhouse Internal Environment

_{i}(t + 1)) and relative humidity (RH

_{i}(t + 1)) based on current information on six meteorological factors, including external temperature (T

_{o}), external relative humidity (RH

_{o}), external insolation (par

_{o}) and wind speed (WS), internal temperature (T

_{i}), and internal relative humidity (RH

_{i}) (Figure 3b). The construction of the BPNN prediction model was based on a total of 1488 hourly IoT data, where 64, 16, and 20% of the data were shuffled and randomly allocated into training, validation, and testing stages, respectively. The architecture of the BPNN model constructed in this study is illustrated in Figure 3b. The parameter setting of the BPNN model is shown in Table 2, where the number of neurons in the hidden layer and the batch size were determined to be 20 and 64, respectively, through trial-and-error processes. The relevant trial-and-error results are presented in Table 3 and Table 4.

#### 2.4. Construction of the Spray Mechanism

^{2}). The weight of spray each time would be 0.001 kg per sprayer, and the total weight of spray per hour would be 1.35 kg for three sprayers. Therefore, the control loop would be evaluated at a rate of 8 s.

#### 2.5. Evaluation of Model Performances

^{2}) as the statistical indicators to evaluate model performance. Their mathematical formulas refer to Equations (20) and (21).

_{i}is the output value of the model, o

_{i}is the observation value, and $\overline{\mathrm{y}}$ and $\overline{\mathrm{o}}$ are the average of the output value and the observation value, respectively.

^{2}value but a lower RMSE value than the comparative model(s).

## 3. Results

#### 3.1. Comparison of Model Accuracy and Reliability between the Physically Based and ANN Models

^{2}and RMSE values of the internal temperature were 0.80 and 1.89 °C, respectively, whereas those of the internal relative humidity were 0.79 and 8.17%, respectively. The results demonstrate the accuracy and reliability of the physically based model. As for the BPNN prediction model, its R

^{2}and RMSE values of the internal temperature were 0.83 and 1.37 °C, respectively, whereas those of the internal relative humidity were 0.88 and 3.9%, respectively. The results also demonstrate the accuracy and reliability of the BPNN model. It appears that the BPNN model is superior to the physically based model in terms of higher R

^{2}and lower RMSE values.

#### 3.2. Comparison of the Spray Effect of Traditional and Smart Control Systems on Greenhouse Internal Environment

#### 3.3. Comparison of Resource Consumption between Traditional and Smart Microclimate-Control Systems

^{2}. Considering the greenhouse investigated in this study occupies an area of 1560 m

^{2}, it would require three sprayers to cover the entire greenhouse farm.

## 4. Discussion

#### 4.1. Evaluation of Hazard Mitigation by the SMCS

^{2}) × 10,000) × 2000 ha/1000) and 771,795 kWh (=((60.2 kWh/1560 m

^{2}) × 10,000) × 2000 ha), respectively (Table 8). This suggests the smart greenhouse microclimate-control practice bears high potential for tackling climate change and can significantly promote the nexus synergies among water, energy, and food, especially when encountering extreme weather events.

#### 4.2. Conributions of the SMCS

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Model construction of the proposed smart microclimate-control system (SMCS) for greenhouse cultivation in consideration of the spray effect. (

**a**). SD model. (

**b**). BPNN prediction model. (

**c**). Physically based estimation model.

**Figure 4.**Spray-simulation flow chart of the SMCS that integrates the SD model, the BPNN prediction model, the physically based estimation model, and the spray mechanism.

**Figure 5.**Errors and error distributions of greenhouse microclimate estimates from the physically based model (20 May 2019–20 July 2019). (

**a**). Internal temperature. (

**b**). Internal relative humidity.

**Figure 6.**Errors and error distributions of greenhouse microclimate predictions from the BPNN model (20 May 2019–20 July 2019). (

**a**). Internal temperature. (

**b**). Internal relative humidity.

**Table 1.**IoT monitoring data collected in this study for model-validation purposes (20 May–20 July 2019 at a 10 min scale).

Item | Notation | SI Unit |
---|---|---|

External temperature | T_{o} | °C |

External relative humidity | RH_{o} | % |

External insolation | par_{o} | W/m^{2} |

Wind speed | WS | m/s |

Wind direction | WD | ° |

Internal temperature | T_{i} | °C |

Internal relative humidity | RH_{i} | % |

Item | BPNN |
---|---|

Number of hidden neurons | 10, 20, 40 |

Number of epochs | 200 |

Early stopping | 20 |

Batch size | 8, 16, 32, 64 |

Learning rate | 0.001 |

Activation function | Scaled exponential linear unit (SELU) |

Optimizer | Adam |

Number of Hidden Neurons | Temperature | Relative Humidity | ||
---|---|---|---|---|

R^{2} | RMSE | R^{2} | RMSE | |

10 | 0.80 | 1.61 °C | 0.87 | 4.45% |

20 ^{1} | 0.82 | 1.55 °C | 0.88 | 4.19% |

40 | 0.81 | 2.42 °C | 0.87 | 4.40% |

^{1}The number of hidden neurons that was determined for constructing the BPNN model in consideration of the model complexity and the values of the evaluation indicators.

Batch Number | Temperature | Relative Humidity | ||
---|---|---|---|---|

R^{2} | RMSE | R^{2} | RMSE | |

8 | 0.83 | 2.08 °C | 0.88 | 4.28% |

16 | 0.81 | 1.56 °C | 0.87 | 4.53% |

32 | 0.82 | 1.67 °C | 0.88 | 4.35% |

64 ^{1} | 0.83 | 1.55 °C | 0.88 | 4.19% |

^{1}The batch number that was determined for constructing the BPNN model in consideration of the values of the evaluation indicators.

**Table 5.**Performance of the physically based estimation model and the BPNN prediction model with respect to greenhouse internal temperature and relative humidity based on test datasets.

Indicators | Temperature | Relative Humidity | ||
---|---|---|---|---|

Physically Based | BPNN | Physically Based | BPNN | |

R^{2} | 0.80 | 0.83 | 0.79 | 0.88 |

RMSE | 1.89 °C | 1.37 °C | 8.17% | 3.9% |

**Table 6.**Results of greenhouse environmental control on internal temperature and relative humidity before and after spraying for environmental cooling by the traditional spraying system (20 May 2019–20 July 2019).

Indicators | Temperature | Relative Humidity | ||
---|---|---|---|---|

Before | After | Before | After | |

Max | 38.8 °C | 28.1 °C | 100% | 100% |

Min | 23.8 °C | 23.8 °C | 37% | 56% |

Average | 29.6 °C | 27.0 °C | 72% | 86% |

Standard deviation | 3.6 °C | 1.3 °C | 16% | 7% |

**Table 7.**Results of greenhouse environmental control on internal temperature and relative humidity before and after spraying for environmental cooling by the SMCS (20 May 2019–20 July 2019).

Indicators | Temperature | Relative Humidity | ||
---|---|---|---|---|

Before | After | Before | After | |

Max | 34.3 °C | 32.9 °C | 91% | 100% |

Min | 21.3 °C | 22.1 °C | 48% | 69% |

Average | 28.0 °C | 26.6 °C | 74% | 89% |

Standard deviation | 2.9 °C | 1.5 °C | 12% | 4% |

**Table 8.**Comparison between traditional and smart microclimate-control systems regarding the resource consumption of spraying for environmental cooling.

Item | Water (kg) | Electric Power (kWh) | Number of On/Off Switch of Sprayers |
---|---|---|---|

Traditional spraying system | 129,478 | 90.0 | 736/1488 |

Smart spraying system | 42,962 | 29.8 | 726/1488 |

Resource-saving amount ^{1} | 86,516 | 60.2 | 10/- |

Resource-saving rate ^{2} | 66.8% | 66.8% | 1.4%/- |

Traditional spraying system | 129,478 | 90.0 | 736/1488 |

^{1}Amount of the traditional spraying system—amount of the SMCS.

^{2}Resource saving amount/amount of the traditional spraying system.

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**MDPI and ACS Style**

Chen, T.-H.; Lee, M.-H.; Hsia, I.-W.; Hsu, C.-H.; Yao, M.-H.; Chang, F.-J.
Develop a Smart Microclimate Control System for Greenhouses through System Dynamics and Machine Learning Techniques. *Water* **2022**, *14*, 3941.
https://doi.org/10.3390/w14233941

**AMA Style**

Chen T-H, Lee M-H, Hsia I-W, Hsu C-H, Yao M-H, Chang F-J.
Develop a Smart Microclimate Control System for Greenhouses through System Dynamics and Machine Learning Techniques. *Water*. 2022; 14(23):3941.
https://doi.org/10.3390/w14233941

**Chicago/Turabian Style**

Chen, Ting-Hsuan, Meng-Hsin Lee, I-Wen Hsia, Chia-Hui Hsu, Ming-Hwi Yao, and Fi-John Chang.
2022. "Develop a Smart Microclimate Control System for Greenhouses through System Dynamics and Machine Learning Techniques" *Water* 14, no. 23: 3941.
https://doi.org/10.3390/w14233941