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The artificial neural network model to estimate the photovoltaic modul efficiency for all regions of the Turkey

机译:人工神经网络模型可估算土耳其所有地区的光伏组件效率

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Artificial neural network (ANN) is a useful tool that using estimates behavior of the most of engineering applications. In the present study, ANN model has been used to estimate the temperature, efficiency and power of the Photovoltaic module according to outlet air temperature and solar radiation. An experimental system consisted photovoltaic module, heating and cooling sub systems, proportional integral derivative (PID) control unit was designed and built. Tests were realized at the outdoors for the constant ambient air temperatures of photovoltaic module. To preserve ambient air temperature at the determined constant values as 10, 20, 30 and 40℃, cooling and heating subsystems which connected PID control unit were used in the test apparatus. Ambient air temperature, solar radiation, back surface of the photovoltaic module temperature was measured in the experiments. Obtained data were used to estimate the photovoltaic module temperature, efficiency and power with using ANN approach for all 7 region of the Turkey. The study dealing with this paper not only will beneficial for the limited region but also in all region of Turkey which will be thought established of photovoltaic panels by the manufacturer, researchers and etc.
机译:人工神经网络(ANN)是一个有用的工具,它可以估算大多数工程应用程序的行为。在本研究中,ANN模型已被用来根据出口空气温度和太阳辐射估算光伏组件的温度,效率和功率。设计并建立了一个实验系统,该系统包括光伏模块,加热和冷却子系统,比例积分微分(PID)控制单元。在室外对光伏模块的恒定环境空气温度进行了测试。为了将环境空气温度保持在确定的恒定值10、20、30和40℃,在测试设备中使用了与PID控制单元相连的冷却和加热子系统。在实验中测量环境温度,太阳辐射,光伏模块背面温度。使用获得的数据通过ANN方法估算土耳其所有7个地区的光伏组件温度,效率和功率。涉及本文的研究不仅将对有限的地区有益,而且还将对土耳其的所有地区都有益,制造商,研究人员等将认为这是建立光伏面板的基础。

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