Multiple neural control of a greenhouse

Fourati, Fathi

doi:10.1016/j.neucom.2014.02.052

Cited by 53 publications

(25 citation statements)

References 14 publications

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“…The better solutions of the proposed ANN model are tested by using the values of a summer period, in order to compare with a complex literature approach [22], that used several multilayer feed-forward neural networks that are trained to model each subsystem in order to achieve a multiple neural control of the greenhouse. It introduces a multiple neural control for the greenhouse micro-climate which consists of the division of the greenhouse control phase in periods where a suitable controller is selected to drive the internal climate.…”

Section: Performance Evaluation Of the Pcfsmentioning

confidence: 99%

“…The approach of Fourati [22] is more complex, indeed the possible implementation is not shown in the paper, instead, usually several literature papers show the possibilities to implement simple ANN, such as the proposed model, by using (Commercial) Off-the-Shelf (COTS ) devices [51,52]. The difference between the proposed approach and Fourati approach is equal to approximately 1 × 10 −1 .…”

Section: Performance Evaluation Of the Pcfsmentioning

confidence: 99%

“…In these terms, it would be good to include information on the fuzzy logic control rules. Moreover, a single FLC [22] for fan coil regulation would be not sufficient to meet the greenhouse requirements in terms of crops production; a better control of inlet air speed and its temperature could improve production.…”

Section: Introductionmentioning

confidence: 99%

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An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

2017

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Abstract:In the recent past home automation has been expanding its objectives towards new solutions both inside the smart home and in its outdoor spaces, where several new technologies are available. This work has developed an approach to integrate intelligent microclimatic greenhouse control into integrated home automation. Microclimatic control of greenhouses is a critical issue in agricultural practices, due to often common sudden daily variation of climatic conditions, and to its potentially detrimental effect on plant growth. A greenhouse is a complex thermodynamic system where indoor temperature and relative humidity have to be closely monitored to facilitate plant growth and production. This work shows an adaptive control system tailored to regulate microclimatic conditions in a greenhouse, by using an innovative combination of soft computing applications. In particular, a neural network solution has been proposed in order to forecast the climatic behavior of greenhouse, while a parallel fuzzy scheme approach is carried out in order to adjust the air speed of fan-coil and its temperature. The proposed combined approach provides a better control of greenhouse climatic conditions due to the system's capability to base instantaneous solutions both on real measured variables and on forecasted climatic change. Several simulation campaigns were carried out to perform accurate neural network and fuzzy schemes, aimed at obtaining respectively a minimum forecasted error value and a more appropriate fuzzification and de-fuzzification process. A Matlab/Simulink solution implemented with a combined approach and its relevant obtained performance is also shown in present study, demonstrating that through controlled parameters it will be possible to maintain a better level of indoor climatic conditions. In the present work we prove how with a forecast of outside temperature at the next time-instant and rule-based controller monitoring of cooling or heating air temperatures and air velocities of devices that regulate the indoor micro-climate inside, a better adjustment of the conditions of comfort for crops is achievable.

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Section: Performance Evaluation Of the Pcfsmentioning

confidence: 99%

Section: Performance Evaluation Of the Pcfsmentioning

confidence: 99%

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An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

2017

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“…These settings are generated in real-time from the measured data and user requirements. This system has the capability to serve as basis for research and testing of various advanced control strategies [9]. …”

Section: A Experimental Greenhouse Systemmentioning

confidence: 99%

A neural network dynamic model for temperature and relative humidity control under greenhouse

Outanoute

Lachhab

Ed-Dahhak

et al. 2015

2015 Third International Workshop on RFID and Adaptive Wireless Sensor Networks (RAWSN)

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This paper deals with the development of dynamic models for the estimations of internal temperature and relative humidity of a greenhouse. Multilayers perceptron with 12 hidden neurons with a hyperbolic tangent as an activation function and which has been trained with Levenberg Marquardt (LM) algorithm. The data used to compute the simulation model were acquired in an experimental greenhouse using a sampling time interval of 10 seconds. The greenhouse is automated with several sensors and actuators that were connected to an acquisition and control system based on a personal computer. A comparison of measured and simulated data for both temperature and relative humidity under greenhouse showed that the elaborated models were able to identify and forecast inside greenhouse conditions reasonably well.

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“…However, there is great uncertainty about the selection of model parameters in the traditional greenhouse modeling process, and the model is not universal once it is established. It should be noted that the most commonly used method of black-box modeling for a nonlinear system was based on neural network, which was applied to establish the greenhouse model by Patil et al [9], Ferreira et al [10], Nabavi-Pelesaraei et al [11], Kavga and Kappatos [12], Fourati [13] and Frausto and Pieters [14]. Trejoperea et al [15] estimated greenhouse energy consumption by using neural networks, and proved that the model gave a better estimation of energy consumption, with an accuracy of 95%.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Prediction of a Greenhouse and Optimization of Daily Average Temperature

Shen

Wei

2018

Energies

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Greenhouses are high energy-consuming and anti-seasonal production facilities. In some cases, energy consumption in greenhouses accounts for 50% of the cost of greenhouse production. The high energy consumption has become a major factor hindering the development of greenhouses. In order to improve the energy efficiency of the greenhouse, it is important to predict its energy consumption. In this study, the energy consumption mathematical model of a Venlo greenhouse is established based on the principle of energy conservation. Three optimization algorithms are used to identify the parameters which are difficult to determine in the energy consumption model. In order to examine the accuracy of the model, some verifications are made. The goal of achieving high yield, high quality and high efficiency production is a problem in the study of greenhouse environment control. Combining the prediction model of greenhouse energy consumption with the relatively accurate weather forecast data for the next week, the energy consumption of greenhouse under different weather conditions is predicted. Taking the minimum energy consumption as the objective function, the indoor daily average temperatures of 7 days are optimized to provide the theoretical reference for the decision-making of heating in the greenhouse. The results show that the optimized average daily temperatures save 9% of the energy cost during a cold wave.

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Multiple neural control of a greenhouse

Cited by 53 publications

References 14 publications

An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

An Innovative Adaptive Control System to Regulate Microclimatic Conditions in a Greenhouse

A neural network dynamic model for temperature and relative humidity control under greenhouse

Energy Consumption Prediction of a Greenhouse and Optimization of Daily Average Temperature

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