Modeling of Energy Demand of a High-Tech Greenhouse in Warm Climate Based on Bayesian Networks

This work analyzes energy demand in a High-Tech greenhouse and its characterization, with the objective of building and evaluating classification models based on Bayesian networks. The utility of these models resides in their capacity of perceiving relations among variables in the greenhouse by identifying probabilistic dependences between them and their ability to make predictions without the need of observing all the variables present in the model. In this way they provide a useful tool for an energetic control system design. In this paper the acquisition data system used in order to collect the dataset studied is described. The energy demand distribution is analyzed and different discretization techniques are applied to reduce its dimensionality, paying particular attention to their impact on the classification model’s performance. A comparison between the different classification models applied is performed

Closed-loop agriculture systems meta-research using text mining

The growing global population and climate change threaten the availability of many critical resources, and have been directly impacting the food and agriculture sector. Therefore, new cultivation technologies must be rapidly developed and implemented to secure the world's future food needs. Closed-loop greenhouse agriculture systems provide an opportunity to decrease resource reliance and increase crop yield. Greenhouses provide versatility in what can be grown and the resources required to function. Greenhouses can become highly efficient and resilient through the application of a closed-loop systems approach that prioritizes repurposing, reusing, and recirculating resources. Here, we employ a text mining approach to research the available research (meta-research) and publications within the area of closed-loop systems in greenhouses. This meta-research provides a clearer definition of the term “closed-loop system” within the context of greenhouses, as the term was previously vaguely defined. Using this meta-research approach, we identify six major existing research topic areas in closed-loop agriculture systems, which include: models and controls; food waste; nutrient systems; growing media; heating; and energy. Furthermore, we identify four areas that require further urgent work, which include the establishment of better connection between academic research to industry applications; clearer criteria surrounding growing media selection; critical operational requirements of a closed-loop system; and the functionality and synergy between the many modules that comprise a closed-loop greenhouse systems

Energy performance and climate control in mechanically ventilated greenhouses: A dynamic modelling-based assessment and investigation

Controlled environment agriculture in greenhouse is a promising solution for meeting the increasing food demand of world population. The accurate control of the indoor environmental conditions proper of greenhouses enhances high crop productivity but, contemporarily, it entails considerable energy consumption due to the adoption of mechanical systems. This work presents a new modelling approach for estimating the energy consumption for climate control of mechanically ventilated greenhouses. The novelty of the proposed energy model lies in its integrated approach in simulating the greenhouse dynamics, considering the dynamic thermal and hygric behaviour of the building and the dynamic response of the cultivated crops to the variation of the solar radiation. The presented model simulates the operation of the systems and the energy performance, considering also the variable angular speed fans that are a new promising energy-efficient technology for this productive sector. The main outputs of the model are the hourly thermal and electrical energy use for climate control and the main indoor environmental conditions. The presented modelling approach was validated against a dataset acquired in a case study of a new fully mechanically controlled greenhouse during a long-term monitoring campaign. The present work contributes to increase the knowledge about the dynamics and the energy consumption of greenhouses, and it can be a valuable decision support tool for industry, farmers, and researchers to properly address an energy efficiency optimisation in mechanically ventilated greenhouses to reach the overall objective of decreasing the rising energy consumption of the agricultural sector

Identification of Dynamic Systems Using Bayesian Networks

Cílem této práce je vytvoření spojení mezi Bayesovskými sítěmi a parametrickou identifikací dynamických systémů. Nejprvé byl zpracován průzkum dostupné literatury a byly zformulovány důležité teoretické základy. Poté jsou uvedeny modely dynamických systémů na bázi Bayesovských sítí. Těžištěm práce je návrh a ověření metodologie identifikace dynamických systémů pomocí Bayesovských sítí. Součástí metodologie je nový přístup k volbě řádu výsledného modelu. Na závěr, byla ověřena navržená metoda identifikace dynamických systémů pomocí Bayesovských sítí na fyzikálních modelech dynamických systémů.Obecně je možno konstatovat, že je disertační práce zaměřena na návrh nového přístupu k identifikaci dynamických systémů ovlivněných šumem. Uvedené modely dynamických systémů na bázi Bayesovských sítí mohou být také využité k estimaci stavu, sledování a řízení dynamických systémů.The aim of this thesis is to provide the bridging between Bayesian networks and system identification. Firstly, the literature review and necessary theoretical prerequisites are provided. Secondly, Bayesian network based models of dynamic systems are introduced. Next, the methodology of Bayesian network based system identification is proposed and explored on simulated datasets. In addition, a new approach to the order selection for a resulting model is proposed and verified. Finally, the proposed Bayesian network based system identification approach is verified on real dynamic systems.Overally, the thesis proposes a new approach to system identification of dynamic systems influenced by noisy signals. In addition, Bayesian network based models proposed in this thesis can be used for state estimation, monitoring and control of dynamic systems

Modelling language for biology with applications

Understanding the links between biological processes at multiple scales, from molecular regulation to populations and evolution along with their interactions with the environment, is a major challenge in understanding life. Apart from understanding this is also becoming important in attempts to engineer traits, for example in crops, starting from genetics or from genomes and at different environmental conditions (genotype x environment → trait). As systems become more complex relying on intuition alone is not enough and formal modelling becomes necessary for integrating data across different processes and allowing us to test hypotheses. The more complex the systems become, however, the harder the modelling process becomes and the harder the models become to read and write. In particular intuitive formalisms like Chemical Reaction Networks are not powerful enough to express ideas at higher levels, for example dynamic environments, dynamic state spaces, and abstraction relations between different parts of the model. Other formalisms are more powerful (for example general purpose programming languages) but they lack the readability of more domain specific approaches. The first contribution of this thesis is a modelling language with stochastic semantics, Chromar, that extends the visually intuitive formalisms of reactions, in which simple objects, called agents, are extended with attributes. Dynamics are given as stochastic rules that can operate on the level of agents (removing/adding) or at the level of attributes (updating their values). Chromar further allows the seamless integration of time and state functions with the normal set of expressions – crucial in multi-scale plant models for describing the changing environment and abstractions between scales. This leads to models that are both formal enough for simulations and easy to read and write. The second contribution of this thesis is a whole-life-cycle multi-model of the growth and reproduction of Arabidopsis Thaliana, FM-life, expressed in a declarative way in Chromar. It combines phenology models from ecology to time developmental processes and physical development, which allows to scale to the population and address ecological questions at different genotype x environment scenarios. This is a step in the path for mechanistic links between genotype x environment and higher-level crop traits. Finally, I show a way of using optimal control techniques to engineer traits of plants by controlling their growth environmental conditions. In particular we explore (i) a direct problem where the control is temperature – assuming homogeneous growth conditions and (ii) indirect problem where the control is the position of the plants – assuming inhomogeneous growth conditions