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Energies
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Advanced Control Systems for Intelligent and Sustainable Operation of Greenhouses
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Advanced Control Systems for Intelligent and Sustainable Operation of Greenhouses

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 5960

Special Issue Editors

Prof. Chiara Bersani

E-Mail Website
Guest Editor
DIBRIS – Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, 16145 Genova, Italy
Interests: energy and environment; optimal control; optimization; systems engineering applied to transport
Dr. Enrico Zero

E-Mail Website
Guest Editor
DIBRIS – Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, 16145 Genova, Italy
Interests: automated systems; optimal control; model predictive control (MPC); BCI

Special Issue Information

Dear Colleagues,

Protected cultivation under greenhouses has created new perspectives offering economical land use, as well as carrying out important energy and water savings. In modern precision agriculture, greenhouses play an increasingly role in meeting the demand energy-driven economy. However, despite the high production crop that can be achieved in a greenhouse, it is one of the most consuming sectors in horticultural business. Consequently, any saving in the energy use of greenhouses leads to significant impacts towards sustainable operation.

Satellite positioning systems, geographic information systems (GIS), control/automation methods and tools, remote sensing, mobile applications, advanced information processing, and telecommunications have been recently applied to this sector in order to optimize energy efficiency and improve the profitability and sustainability of agricultural production while preserving natural resources.

The proposed Special Issue aims to propose the current state of the art of precision agriculture in the greenhouse context, focusing on the most innovative approaches in term of challenges, requirements, methodologies, technologies and smart applications, to establish the best climate parameters resulting in higher quality crop growths, with energy and water saving minimizing the use of pesticides and chemical products. 

Moreover, recently, interest in smart grids has increased extensively around the world.  The main advantage of smart grids deployment is to provide customers with a bidirectional communication platform that allows them to send, receive, save, and even control their energy needs and excesses. In this framework, this Special Issue also addresses and providees an outline of the application of smart grids technologies, and advanced control and optimization methods in the precision agriculture sector.

Specific topics of interest for the Special Issue include, amongst others:

  • New methodologies for energy efficiency in greenhouses;
  • Model predictive control in smart greenhouse;
  • Distributed optimization in smart greenhouse;
  • Control/automation methods and tools for the sustainable operation of greenhouses;
  • Real time monitoring in smart agriculture;
  • IoT in smart greenhouse;
  • Smart energy management systems of greenhouses;
  • Smart greenhouses integrated microgrid;
  • Smart grids applied to precision agriculture;
  • Smart microgrid and renewable energy applications in precision agriculture;
  • Novel control strategies and algorithms with a particular application to precision agriculture;
  • Decision support systems for precision agriculture;
  • Advanced materials for greenhouse applications;
  • Case studies and innovative applications for precision agriculture;
  • Planning and designs for smart and precision agriculture;
  • Precision agriculture and crop protection;
  • Smart greenhouse modelling;
  • Artificial intelligence applied to smart greenhouses;
  • Water harvesting management;
  • Applications of unmanned aerial systems;
  • Satellite-based applications;
  • Wireless sensor networks and Internet of Things;
  • Software and mobile applications for precision agriculture.

Prof. Chiara Bersani
Dr. Enrico Zero
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart greenhouse
  • Climate monitoring and control
  • Energy management
  • Smart sensing
  • Model predictive control
  • Distributed optimization
  • Microgrid
  • Renewable energy

Published Papers (2 papers)

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15 pages, 3950 KiB  
Article
Evaluation of Supervised Learning Models in Predicting Greenhouse Energy Demand and Production for Intelligent and Sustainable Operations
by Laila Ouazzani Chahidi, Marco Fossa, Antonella Priarone and Abdellah Mechaqrane
Energies 2021, 14(19), 6297; https://doi.org/10.3390/en14196297 - 02 Oct 2021
Cited by 11 | Viewed by 1810
Abstract
Plants need a specific environment to grow and reproduce in fine fettle. Nevertheless, climatic conditions are not stable and can impact their well-being and, consequently, harvest quality. Thus, greenhouse cultivation is one of the suitable agricultural techniques for creating and controlling the inside [...] Read more.
Plants need a specific environment to grow and reproduce in fine fettle. Nevertheless, climatic conditions are not stable and can impact their well-being and, consequently, harvest quality. Thus, greenhouse cultivation is one of the suitable agricultural techniques for creating and controlling the inside microclimate to be adequate for plant growth. The relevance of greenhouse control is widely recognized. The prediction of greenhouse variables using artificial intelligence methods is of great interest for intelligent control and the potential reduction in energetic and financial losses. However, the studies carried out in this context are still more or less limited and several machine learning methods have not been sufficiently exploited. The aim of this study is to predict the air conditioning electrical consumption and photovoltaic module electrical production at the smart Agro-Manufacturing Laboratory (SamLab) greenhouse, located in Albenga, north-western Italy. Different supervised machine learning methods were compared, namely, Artificial Neural Networks (ANNs), Gaussian Process Regression (GPR), Support Vector Machine (SVM) and Boosting trees. We evaluated the performance of the models based on three statistical indicators: the coefficient of correlation (R), the normalized root mean square error (nRMSE) and the normalized mean absolute error (nMAE). The results show good agreement between the measured and predicted values for all models, with a correlation coefficient R > 0.9, considering the validation set. The good performance of the models affirms the importance of this approach and that it can be used to further improve greenhouse efficiency through its intelligent control. Full article
(This article belongs to the Special Issue Advanced Control Systems for Intelligent and Sustainable Operation of Greenhouses)
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21 pages, 4256 KiB  
Article
Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse
by Chiara Bersani, Marco Fossa, Antonella Priarone, Roberto Sacile and Enrico Zero
Energies 2021, 14(11), 3353; https://doi.org/10.3390/en14113353 - 07 Jun 2021
Cited by 11 | Viewed by 3182
Abstract
The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in [...] Read more.
The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in order to control the indoor air temperature in a high efficiency greenhouse. The main objective is to determine the optimal control signals related to the water mass flow rate supplied by a heat pump. The MPC model allows a predefined temperature profile to be tracked with an energy saving approach. The MPC has been implemented as a multiobjective optimization model that takes into account the dynamic behavior of the greenhouse in terms of energy and mass balances. The energy supply is provided by a ground coupled heat pump (GCHP) and by the solar radiation while the energy losses related to heat transfers across the glazed envelope. The proposed MPC method was applied in a smart innovative greenhouse located in Italy, and its performances were compared with a traditional reactive control method in terms of deviation of the indoor temperature in respect to the desired one and in terms of electric power consumption. The results demonstrated that, for a time horizon of 20 h, in a greenhouse with dimensions 15.3 and 9.9 m and an average height of 4.5 m, the proposed MPC approach saved about 30% in electric power compared with a relay control, guaranteeing a consistent and reliable temperature profile in respect to the predefined tracked one. Full article
(This article belongs to the Special Issue Advanced Control Systems for Intelligent and Sustainable Operation of Greenhouses)
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