Greenhouse Temperature Control Research Articles

Comparative study for enhanced temperature control in smart greenhouses

This paper introduces a comparative study of four distinct controllers intended for efficient temperature regulation in smart greenhouses. Beginning with an overview of the general research background, the paper addresses the specific challenges inherent in greenhouse temperature control, as well as the different techniques used for regulation. Utilizing Matlab Simulink software, the greenhouse temperature control system is simulated. The study evaluates the performance of each controller in maintaining the internal temperature within the desired range amidst varying external conditions. Previous research has shown that all four controllers effectively maintain the desired temperature range, although their performance varies in terms of power consumption, precision, robustness, and response time, depending primarily on the project requirements and goals. Our contribution primarily consists of studying all these types of controllers and determining the best controller to choose for regulating the internal temperature of the greenhouse.

Low-carbon economic scheduling for AIES considering flexible load coordination and multiple uncertainties

The disordered energy use in traditional agricultural production has resulted in high energy purchase costs and carbon emissions. The coupling of integrated energy systems with agriculture offers the potential for flexible coordination of agricultural loads while agricultural loads are highly influenced by changes in the natural environment. In this context, this paper proposes a low-carbon and economic scheduling optimization method for agricultural integrated energy system (AIES), which comprehensively takes temperature, light intensity, and the coordination of agricultural loads into consideration. Firstly, solar and biomass resources are integrated through energy conversion equipment to support energy supply from both the electrical distribution network (EDN) and the gas network. At the same time, the modelling process of agricultural load is refined, which is divided into crop irrigation, greenhouse temperature control and residential consumption. The models of agricultural multi-energy complementary and energy storage equipment are established to achieve flexible conversion between different energy sources. In addition, the distributionally robust optimization (DRO) method based on multiple discrete scenarios is adopted to deal with the uncertainties arising from environmental condition fluctuations during the optimization process. Finally, the proposed model is tested on 4 various regions and 2 different seasons. Numerical results show that the proposed model can effectively enhance the economic and environmental performance of the AIES.

Design of MFAC greenhouse temperature controller based on LESO with disturbance estimation and fuzzy controller with parameter tuning

The proper temperature is essential for the growth and development of greenhouse crops. Therefore, it is necessary to determine an appropriate greenhouse temperature automatic control strategy. In this paper, a model-free adaptive greenhouse temperature control strategy is proposed by combining a linear extended state observer and a fuzzy controller. Specifically, the linear extended state observer is introduced to estimate model approximation error generated by pseudo gradient estimation. The fuzzy controller is used to correct the system parameter ρ online. The simulation results show that when there is no disturbance, compared with the typical model-free adaptive scheme, the MAE and RMSE of the greenhouse temperature control scheme proposed in this paper are reduced by 57.55% and 5.40% respectively. In the presence of custom disturbances, the proposed control scheme still has a faster response speed, stronger robustness, and higher control accuracy. And the proposed method can reduce the frequent action of the actuator, thus effectively reducing the energy cost.

Research on improved partial format MFAC greenhouse temperature control method based on low energy consumption optimization

Temperature is critical to the growth of crops in agricultural greenhouses. Thus, designing a greenhouse temperature controller that maximizes energy savings while maintaining control accuracy is very important. This paper proposes an improved partial format model-free adaptive control method and designs a greenhouse temperature controller based on this method to balance control accuracy and energy consumption. Firstly, a limited energy consumption term is added to the control input cost function of the traditional partial format model-free adaptive control to penalize excessive control input. We derive an improved partial format model-free adaptive control input algorithm and design a greenhouse temperature controller using this algorithm. Then, the controller's sensitive parameters are selected using a Monte Carlo-based parameter sensitivity method. Finally, the whale optimization algorithm is used to optimize the sensitive parameters. The insensitive parameters are set according to experience. This paper, a theoretical study based on simulated experiments, proves the convergent tracking error of the proposed improved partial format model-free adaptive algorithm. Simulation results show that the improved controller minimizes energy consumption while ensuring control accuracy, reducing power consumption by 12.35 % compared to the traditional controller.

Modeling of a Greenhouse Climatic Conditions Control System with PID Controller for Plant Growth Optimization

In modern agriculture, our focus is on optimising crop cultivation, particularly in greenhouses, to enhance productivity and resource efficiency. This paper investigates the pivotal role of temperature and humidity control in greenhouses for optimal plant growth. Our study employs a Matlab/Simulink-designed system for continuous monitoring, analysis, and real-time adjustments, connecting with various actuators and integrating external data. Results show the system efficiently corrects variations, ensuring temperature and humidity return to set points of 25°C and 60% respectively. This underscores the transformative potential of the control system in revolutionizing agriculture for increased efficiency and sustainability.

Data-driven robust model predictive control for greenhouse temperature control and energy utilisation assessment

The greenhouse microclimate, especially temperature, is highly complex, and controlling it requires significant resources due to the greenhouses' inefficient design. The application of model predictive control is a promising strategy for temperature control and efficient greenhouse management. However, it does not account for the inaccuracies and uncertainties existing in the system, leading to sub-optimal temperatures. Therefore, this study proposes a comprehensive data-driven robust model predictive control framework for greenhouse temperature control and its energy utilisation assessment in the presence of uncertainties. First, an analytical model based on mass and energy balance and a data-driven model based on an artificial neural network is developed, and their prediction performance is compared. The artificial neural network demonstrates a higher prediction accuracy and is used as the system model in the proposed control framework. A robust model predictive control strategy, based on the minimax objective function and particle swarm optimisation algorithm, is developed to handle the uncertainties in the system. Results illustrate that in the presence of uncertainties, the robust model predictive control strategy outperforms the existing greenhouse climate management system and basic model predictive control with an RMSE of 0.32 °C and 0.60 °C for a two-day simulation period in winter and summer, respectively. Furthermore, the robust model predictive control strategy leads to an energy reduction of 9.67% and 23.61% in winter and summer. The proposed framework is flexible and general and can be applied to other greenhouses with different configurations and cultivated crops by fine-tuning it on the new data set.

Dual stage cascade controller for temperature control in greenhouse

In this paper, a dual stage cascade controller PI-(1+PD) is adopted to maintain and control temperature in greenhouse environment based on a smart and intelligent gorilla troops optimization (GTO) method for evaluating the controller gains to enhance the system response by reducing the error value and minimize the integral time absolute error (ITAE) fitness functions during simulation. The simulation results are obtained by using MATLAB 2019, then compared with two conventional controllers proportional integral derivative (PI and PID) based on evaluation parameters for all controllers in term of peak time, rise time, settling time and overshoot to show its efficient response if compared with other controllers used.

Temperature prediction of solar greenhouse based on NARX regression neural network

Temperature has an important influence on plant growth and development. In protected agriculture production, accurate prediction of temperature environment is of great significance. However, due to the time series, nonlinear and multi coupling characteristics of temperature, it is difficult to achieve accurate prediction. We proposed a method for building a solar greenhouse temperature prediction model based on a timeseries analysis, that considers the time series characteristics and dynamic temperature changes in the greenhouse system. The method would predict the temperature of greenhouse, and provide reference for the temperature change law in protected agriculture. A parameter analysis was performed on the nonlinear autoregressive exogenous (NARX) neural network, and a solar greenhouse temperature time series prediction model was established using the NARX regression neural network. The results showed that the proposed model depicted a maximum absolute error of 0.67 °C, and model correlation coefficient of 0.9996. Compared with the wavelet and BP neural networks, the NARX regression neural network accurately predicted and significantly outperformed in the solar greenhouse temperature prediction model. Moreover, the prediction model can accurately predict temperature trends within the solar greenhouse and is pivotal to obtaining precise control of solar greenhouse temperature.

Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM

A method of establishing a prediction model of the greenhouse temperature based on time-series analysis and the boosting tree model is proposed, aiming at the problem that the temperature of a greenhouse cannot be accurately predicted owing to nonlinear changes in the temperature of the closed ecosystem of a greenhouse featuring modern agricultural technology and various influencing factors. This model comprehensively considers environmental parameters, including humidity inside and outside the greenhouse, air pressure inside and outside the greenhouse, and temperature outside the greenhouse, as well as time-series changes, to make a more accurate prediction of the temperature in the greenhouse. Experiments show that the R2 determination coefficients of different prediction models are improved and the mean square error and mean absolute error are reduced after adding time-series features. Among the models tested, LightGBM performs best, with the mean square error of the prediction results of the model decreasing by 18.61% after adding time-series features. Comparing with the support vector machine, radial basis function neural network, back-propagation neural network, and multiple linear regression model after adding time-series features, the mean square error is 11.70% to 29.12% lower. Furthermore, the fitting degree of LightGBM is the best among the models. The prediction results of LightGBM therefore have important application value in greenhouse temperature control.

Design and experiment of Internet-of-Things cooling system in glass greenhouse based on computational fluid dynamics simulation

In the summer heat season, the performance of the greenhouse cooling system is the key factor in greenhouse crop pollination and fruit formation. Scientific design of greenhouse cooling systems and intelligent control of cooling equipment can ensure the normal growth of greenhouse crops and save energy. In this paper, the thermal equilibrium theory of the greenhouse is analysed, and the glass greenhouse thermal environment model is established based on the theory of engineering thermophysics combined with greenhouse environmental regulation. This study uses computational fluid dynamics simulation technology to simulate the change of the greenhouse temperature field, perform experimental analysis, and scientifically design an intelligent greenhouse temperature control cooling system. It provides a reference for designing an internet of things cooling system in a glass greenhouse in theoretical analysis and engineering practice.

Design and experiment of IOT cooling system in glass greenhouse based on CFD simulation

In the summer heat season, the performance of greenhouse cooling system is the key factor of the greenhouse crop pollination and fruit formation. Scientific design of greenhouse cooling system and intelligent control of cooling equipment can ensure the normal growth of greenhouse crops and save energy. In this paper, the thermal equilibrium theory of greenhouse is analyzed, and the thermal environment model of glass greenhouse is established based on the theory of engineering thermophysics combined with greenhouse environmental regulation. This study uses CFD (Computational Fluid Dynamics) simulation technology to simulate the change of greenhouse temperature field and perform experimental analysis, and designs an intelligent greenhouse temperature control cooling system scientifically. It provides reference for the design of IOT (Internet of Things) cooling system in glass greenhouse in theoretical analysis and engineering practice.

Intelligent Temperature Control System of Greenhouse Based on STM32 Single Chip Microcomputer

With the development of automatic control technology and the popularization of agricultural science and technology, people are more and more aware of the intelligence convenience. In this study, a set of intelligent greenhouse temperature control system is established based on single chip microcomputer STM32F103. PID algorithm is used to realize stepless adjustment of greenhouse temperature, and complete the temperature automatic monitoring and control. It solves the greenhouse problems of low automation degree, large amount of manual work and low control accuracy of crop growth environment. The system simulation and practice show that it can improve the temperature and humidity control accuracy of greenhouse by 10.8%, and reduce the amount of manual work, and provide a good natural environment for plant growth and development, and have high economic benefits.

A study on temperature spatial distribution of a greenhouse under solar load with considering crop transpiration and optical effects

Spatial temperature distribution of a greenhouse is critical to precision agriculture management, especially for the vertical cultivation mode. Crop transpiration and optical effects influence spatial temperature distribution of a greenhouse, which, however, were often omitted in literature. In this work, a Computational Fluid Dynamics model on studying spatial temperature distribution of a greenhouse was developed with considering the effects of air (light absorption), crop (light absorption, reflection, transmission, and transpiration), and soil (light absorption and heat radiation) in the greenhouse under dynamic solar load based on the law of energy conservation. A set of field tests was used to validate the developed Computational Fluid Dynamics model. Spatial temperature distributions of the greenhouse under different scenarios were simulated with and without considering crop effects. The results show that the temperature standard deviation of the greenhouse with considering the crop effects was about 31.68% higher than that without considering the crop effects. This implies that greenhouse temperature distribution is significantly influenced by crop transpiration and optical effects. The results also show that the highest temperature appears in the air region below the top of greenhouse and changes with the dynamic solar radiation direction, and the temperature may vary by about 63.65% during the day. There is a temperature difference of 2 °C-3 °C at the same height level between the greenhouse with and without considering the crop effects. This work is important for understanding the non-uniform temperature spatial distribution pattern of a greenhouse as affected by crops, and provides information for sensor deployment, monitoring, and control of greenhouse temperature.

Greenhouse temperature control based on Fuzzy Logic

There is a close relationship between crop growth and the control of environmental variables, as well as irrigation and fertilizers supplied. This article presents a system for collecting a greenhouse temperature, capable of acting in the opening or closing window system as a regulator of this environmental variable. Controlling the temperature acting on the opening or closing of the windows is convenient, since it does not require additional fuel, resulting in an economical alternative. Regarding control algorithm, Fuzzy Logic was used as a correction temperature technique. The proposal can be a good option for greenhouses that are not automated yet, saving costs by moving from human-assisted monitoring to automatic temperature monitoring.

A fast and practical one-dimensional transient model for greenhouse temperature and humidity

This paper introduces a new transient greenhouse model which uses a mechanistic method to estimate the temperature and humidity in typical Chinese solar greenhouses. A novel and easy-to-use wall temperature estimation method based on the energy balance was adopted for the environment model rather than using boundary temperature measurements. In this way, the number of model inputs is considerably reduced, and the proposed model is able to predict future greenhouse climate conditions by utilizing only the weather forecast. The model validation was performed in two different greenhouses (each with different sizes and physical parameters, such as the greenhouse volume, the roof and wall areas, the wall materials and so on) on three typical days in 2019 and 2020, and over four consecutive weeks in different seasons during 2016 and 2019 . Promising results were obtained and the model performed well in different operating modes; these included having the vents completely closed, opening the vents, and completely closing the vents in the cold season with an additional thermal insulation blanket covering. The validation results demonstrate that the proposed model can be widely adapted to different sizes of typical Chinese solar greenhouses, as well as to different weather conditions. Thus, the developed model is a flexible and valuable tool that can be used for greenhouse climate simulation, temperature and humidity control, and as a decision-making support system to help manage solar greenhouses.

Model Predictive Temperature and Humidity Control of Greenhouse with Ventilation

Facility gardening with complex environmental control systems has potential in improving productivity and producing high value-added agricultural products; however, its introduction is expensive. Thus, this study proposes a greenhouse temperature and humidity control system using relatively inexpensive heaters, humidifiers, and ventilation fans. Humidity control is expected to promote photosynthesis and prevent plant disease. The concept of model predictive control (MPC) was applied to a small greenhouse to minimise the sum of squared errors. Compared with the two conventional methods, the simulation results showed that MPC reduced the relative RMS error of the temperature and humidity deficit to 23.5% and 13.1%, respectively.

Development of Energy Saving and Rapid Temperature Control Technology for Intelligent Greenhouses

Modern greenhouses need a certain equipment to achieve the expected environmental temperature in a short time while saving energy. To achieve greenhouse temperature management and energy savings through intelligent control, a greenhouse mechanism model is first built to study the controller design for a greenhouse located in Yiyang city in this paper. Then, the model accuracy is verified by experimental data. Based on the verified model, two intelligent control technologies are proposed, namely, active disturbance rejection control and fuzzy active disturbance rejection control. Methods are adopted to adjust skylight opening and thermal air conditioning for greenhouse temperature control and energy savings. In 60 hours of continuous working time, fuzzy active disturbance rejection control takes 10 hours less than active disturbance rejection control to make the greenhouse temperature reach the ideal steady state, and the temperature overshoot is reduced by 60%. Through analysis, the proposed fuzzy active disturbance rejection control method for greenhouse temperature management can achieve 15% energy savings. The proposed intelligent control technology can also be applied to temperature management for real greenhouses.

Research on intelligent control of an agricultural greenhouse based on fuzzy PID control

With the development of agricultural science and technology in China, the number of greenhouses is increasing rapidly, but the control of greenhouse temperature, the most critical factor in the greenhouse environment, can no longer meet the requirements of the management and operation of agricultural greenhouse by manpower. Therefore, the perfect combination of greenhouse control and intelligent technology has become an urgent demand in the field of agricultural greenhouses. Research on the combination of greenhouse control and intelligent technology will help save human and financial resources and achieve the objectives of more efficient and stable control. Finally, it can guarantee an increase in crop yield and the profit of greenhouse operators. In this study, the temperature control of an agricultural greenhouse based on fuzzy proportion, integration and differentiation (PID) was taken as the research subject and a greenhouse temperature model was constructed by mathematical expression. Finally, simulation effect charts under simple fuzzy control and PID control were obtained, and the results were compared. Finally, it is concluded that fuzzy PID temperature control has the advantages of short response time and stable temperature-control effect, which is the optimal intelligent control mode of an agricultural greenhouse.

Study of metrological characteristics of low-cost digital temperature sensors for greenhouse conditions

The article focuses on the relevant scientific and applied problem of assessing and analyzing the metrological characteristics of available digital temperature sensors for greenhouse conditions. The hardware and software implementation of the microprocessor system for obtaining calibration characteristics and evaluating the accuracy and performance of temperature sensors for the physical media under greenhouse conditions is studied. A particular type of linear calibration equation for the temperature sensors under study is established. The values of systematic absolute measurement deviations of temperature sensors DS18B20, SHT11, SHT21, BMP180, BME280 and DHT22 are experimentally obtained. Recommendations on improving the accuracy of temperature information and temperature measuring systems under greenhouse conditions are given. The perspective areas of the research on metrological provision of modern means of automatic monitoring and temperature control in industrial greenhouses are substantiated.

Simulation and Experiments of Active Greenhouse Heating System

This paper proposed an active greenhouse heating system which focused on the problem of crop growth under extreme cold weather. At the beginning, heat transfer process of heating system was analysed based on thermal equilibrium theory. Radial and axial heat conduction mathematical analysis model of temperature control system were established respectively. Then, numerical simulation of temperature distribution was carried out by means of finite element method (FEM) software. When the effective operation range (later referred to as Qf) was defined with soil temperature more than 15 °C at 20 cm underground, opening temperature of heating system should be no less than 28 °C. Simulation results show that an increase of air inlet temperature by 2 °C could extend axial effective operating range by 2.4 m–2.8 m. Later, field test was implemented in greenhouses which located in Changping district, Beijing. Finally, compared with simulation data, the field testing data shows that the perturbation law of heating system heat transfer process on shallow and deep layer soil temperature is consistent with theoretical analysis. The numerical simulation could help to guide the temperature control of greenhouse. DOI: 10.5755/j01.eie.25.2.23205