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Development of a Greenhouse Model with an Intelligent Indoor Environment and Energy Management System for Greenhouses

机译:温室模型与温室智能室内环境和能源管理系统的开发

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The microclimate control in a greenhouse is a difficult and complicated procedure since the factors that modulate the clime are several and dependant of each other. This work is an effort of controlling the most of these factors with a conjunction of the most possible low energy consumption. A microclimate system control is designed based on Artificial Intelligence techniques. Two Fuzzy logic controllers are developed embodies the expert knowledge of the agriculturists and the growers. These controllers consist of fuzzy P (Proportional) and PD (Proportional-Derivative) control using desired clime set points. The factors that being monitored are the Greenhouse's indoor luminance value, temperature, relative humidity, CO_2 concentration and the outside temperature, actuating in automations as heating units, motor-controlled windows, motor controlled shading curtains, artificial lighting, CO_2 enrichment bottles and water fogging valves. These controllers obtain the best possible microclimate for any cultivation setting the desired parameters into the set-points. They prototyped in Matlab environment and tested through a greenhouse Model, which was designed into TRNSYS IISIBAT software. For the Model an algorithm developed predicting ambient greenhouse air conditions to be used for energy efficiency simulation and control schemes optimization. The climatic conditions considered are temperature, relative humidity, CO_2 concentration and solar radiation. The algorithm has two modes of operation, the first simulates the greenhouse while in the second the heating, cooling, humidification or dehumidification, CO_2 injection rates are calculated to maintain certain set points. The algorithm is designed to be used with the TRNSYS 15 simulation software which provides the pre-processing of the weather data, as well as controller models. The model is defined by several components that describe the characteristics of each glazing surface, the plants, the floor, the equipment and the zone itself. Using this approach it is possible to simulate any greenhouse structure, provided that the required information is available.
机译:温室中的微气候控制是一种困难而复杂的程序,因为调制CLIME的因素是彼此的几个又依赖。这项工作是一种控制大多数这些因素的努力,即最可能的低能量消耗的结合。基于人工智能技术设计了一种小气候系统控制。开发了两个模糊的逻辑控制器体现了农业学家和种植者的专业知识。这些控制器包括使用所需的CLIME设定点的模糊P(比例)和PD(比例衍生物)控制。被监控的因素是温室的室内亮度值,温度,相对湿度,CO_2浓度和外部温度,在自动化装置中致动,作为加热装置,电机控制的窗口,电机控制着色窗帘,人工照明,CO_2浓缩瓶和水雾化阀门。这些控制器可以获得任何培养的最佳单流动,将所需参数设置为设定点。它们在Matlab环境中原型设计,并通过温室模型进行测试,该模型被设计成Trnsys Iisibat软件。对于模型,算法开发了预测环境温室空气条件,用于用于能效仿真和控制方案优化。考虑的气候条件是温度,相对湿度,CO_2浓度和太阳辐射。该算法具有两种操作模式,首先模拟温室,而在第二加热,冷却,加湿或除湿中,计算CO_2注射率以维持某些设定点。该算法旨在与TRNSYS 15仿真软件一起使用,该软件提供天气数据的预处理,以及控制器模型。该模型由几个组件定义,该组件描述了每个玻璃表面,植物,地板,设备和区域本身的特性。使用这种方法可以模拟任何温室结构,只要需要所需信息。

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