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

机译:具有智能室内环境和温室能源管理系统的温室模型的开发

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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{sub}2 concentration and the outside temperature, actuating in automations as heating units, motor-controlled windows, motor controlled shading curtains, artificial lighting, CO{sub}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{sub}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{sub}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个困难而复杂的过程,因为调节气候的因素是多种且相互依赖的。这项工作是在控制尽可能多的这些因素的同时,努力实现了尽可能低的能耗。基于人工智能技术设计了微气候系统控件。开发了两个模糊逻辑控制器,体现了农业学家和种植者的专业知识。这些控制器由模糊P(比例)和PD(比例-导数)控制组成,它们使用所需的色标设置点。监视的因素包括温室的室内亮度值,温度,相对湿度,CO {sub} 2浓度和外部温度,作为加热单元,电机控制的窗户,电机控制的遮阳帘,人造照明,CO { sub} 2浓缩瓶和水雾阀。这些控制器可为任何将所需参数设置为设定点的栽培提供最佳的微气候。他们在Matlab环境中进行了原型设计,并通过了温室模型进行了测试,该模型已设计到TRNSYS IISIBAT软件中。对于该模型,开发了一种算法,用于预测温室环境的空气状况,以用于能源效率模拟和控制方案的优化。所考虑的气候条件是温度,相对湿度,CO {sub} 2浓度和太阳辐射。该算法具有两种操作模式,第一种模拟温室,第二种模拟加热,冷却,加湿或除湿,计算CO {sub} 2注入速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,该软件可对天气数据以及控制器模型进行预处理。该模型由描述每个玻璃表面,植物,地板,设备和区域本身的特征的几个组件定义。使用此方法,可以模拟任何温室结构,前提是可以获取所需的信息。

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