Greenhouse climate fuzzy adaptive control considering energy saving

Su, Yuanping; Xu, Lihong; Goodman, Erik D.

doi:10.1007/s12555-016-0220-6

Cited by 31 publications

(19 citation statements)

References 28 publications

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“…It was also found from a comparison of cooling energy consumption between Periods 2 and 3 that hourly energy consumption in Period 3 is significantly less than that in Period 2 (Table A4). The control settings of vents opening and shade screen closing during the day for energy savings could be used to extend the control strategies in greenhouses reported by previous studies [26,28]. Further analysis of energy consumption taking into consideration of temperature profile of the facility and outside temperature under two extreme configuration settings tested (Periods 2 and 3) found that cooling energy consumption can be benchmarked using the cooling energy consumption (kWh) per 1 • C reduction.…”

Section: Discussionmentioning

confidence: 94%

Energy Minimisation in a Protected Cropping Facility Using Multi-Temperature Acquisition Points and Control of Ventilation Settings

et al. 2021

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Energy management in protected cropping is critical due to the high cost of energy use in high-tech greenhouse facilities. The main purpose of this research was to investigate the optimal strategy to reduce cooling energy consumption, by regulating the settings (opening/closing) of either vents or curtains during the day, at the protected cropping facility at Western Sydney University. We measured daily changes in air temperature and energy consumption under four treatments (open/closed combinations of vents and shade screens) and developed an optimal cooling strategy for energy management using multi-temperature acquisition points at different heights within a greenhouse compartment. The optimal treatment (vents open/curtains closed) reduced energy load at the rooftop, thereby maintaining a desirable plant canopy temperature profile, and reducing cooling energy. Daily energy consumption was lowest for vents open/curtains closed (70.5 kWh) and highest for vents closed/curtains open (121 kWh). It was also found that delaying the operation of opening and closing of vents and curtains until the plant canopy temperature reached 25 °C reduced cooling energy consumption and decreased heating energy consumption in the morning (e.g., 08:00 to 10:00). The estimated savings of 1.83 kWh per 1 °C cooling between the optimal (vents open/curtains closed) and least optimal (vents closed/curtains open) conditions had the potential for significant energy savings at 494 kWh per °C over a crop cycle of nine months in warm weather conditions. However, selection of the optimal cooling strategy utilising control of vents and curtains must also account for the impact from other greenhouse environmental factors, including light, humidity, and CO2 concentration, which may be crop specific.

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Section: Discussionmentioning

confidence: 94%

Energy Minimisation in a Protected Cropping Facility Using Multi-Temperature Acquisition Points and Control of Ventilation Settings

et al. 2021

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“…Therefore, it is the motivation of this work to develop an optimal control approach for greenhouse climate. To illustrate the advantage of the proposed approach, we have performed a comparison with AFC, 40 as shown in Figure 7. From Figure 7, we can see that, in most cases, the tracking error of the NNOC is smaller than that of the AFC.…”

Section: Resultsmentioning

confidence: 99%

“…This implies that the weight update laws (21), (40), and (50) of the 3 NN identifiers can guarantee the asymptotical convergence of the closed-loop system and the UUB performance of the tracking error e k .…”

Section: Stability Analysismentioning

confidence: 99%

Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study

Goodman

2017

Optim Control Appl Methods

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Summary Greenhouse production must create a suitable growth environment for its crop to improve yield while minimizing the energy used to maintain the greenhouse environment, thereby reducing production cost. To this end, this paper develops a nearly optimal control approach based on adaptive dynamic programming. In this method, 3 neural networks are used to estimate the value function and control policy and to compensate for the unmodeled dynamics of the greenhouse climate. Taking into account the greenhouse system typically being an overactuated system, the total control efforts for heat, fog, and CO2 are considered as the virtual control inputs to be generated by the optimal controller. To obtain the real control inputs, a control allocation technique is introduced to distribute the virtual control inputs to the actuators. Finally, Lyapunov stability analysis is performed to derive the update law of the neural networks to ensure the asymptotic convergence of the closed‐loop system, and simulation is carried out to illustrate the effectiveness and control performance of the proposed approach.

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“…In addition, the implementation of energy supply optimization strategies is another aspect of efficiently improving the water, energy, and food security in greenhouses. The optimization strategies have varied from feedback conventional controllers to advanced ones based on artificial intelligence such as: fuzzy logic controllers (Su et al 2017), hybrid particle swarm optimization and genetic algorithms (Chen et al 2015), model predictive controllers (Ouammi et al 2020a, b;Lin et al 2020) and optimal controllers (Beveren et al 2015, Beveren et al 2020Singh et al 2015). Their common goal is to maximize production yield by tracking the desired trajectories of the controlled variables while minimizing the use of available water and energy resources.…”

Section: Energy Considerations In Smart Greenhousesmentioning

confidence: 99%

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

Karanisa

Achour²,

Ouammi

et al. 2022

Environ Syst Decis

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Greenhouse farming is essential in increasing domestic crop production in countries with limited resources and a harsh climate like Qatar. Smart greenhouse development is even more important to overcome these limitations and achieve high levels of food security. While the main aim of greenhouses is to offer an appropriate environment for high-yield production while protecting crops from adverse climate conditions, smart greenhouses provide precise regulation and control of the microclimate variables by utilizing the latest control techniques, advanced metering and communication infrastructures, and smart management systems thus providing the optimal environment for crop development. However, due to the development of information technology, greenhouses are undergoing a big transformation. In fact, the new generation of greenhouses has gone from simple constructions to sophisticated factories that drive agricultural production at the minimum possible cost. The main objective of this paper is to present a comprehensive understanding framework of the actual greenhouse development in Qatar, so as to be able to support the transition to sustainable precision agriculture. Qatar’s greenhouse market is a dynamic sector, and it is expected to mark double-digit growth by 2025. Thus, this study may offer effective supporting information to decision and policy makers, professionals, and end-users in introducing new technologies and taking advantage of monitoring techniques, artificial intelligence, and communication infrastructure in the agriculture sector by adopting smart greenhouses, consequently enhancing the Food-Energy-Water Nexus resilience and sustainable development. Furthermore, an analysis of the actual agriculture situation in Qatar is provided by examining its potential development regarding the existing drivers and barriers. Finally, the study presents the policy measures already implemented in Qatar and analyses the future development of the local greenhouse sector in terms of sustainability and resource-saving perspective and its penetration into Qatar’s economy.

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Greenhouse climate fuzzy adaptive control considering energy saving

Cited by 31 publications

References 28 publications

Energy Minimisation in a Protected Cropping Facility Using Multi-Temperature Acquisition Points and Control of Ventilation Settings

Energy Minimisation in a Protected Cropping Facility Using Multi-Temperature Acquisition Points and Control of Ventilation Settings

Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

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