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In this study, an artificial neural network was modeled in order to predict the power generated by a monocrystalline silicon photovoltaic panel. This experimental study measured and recorded the voltage and current generated by the photovoltaic panel for a year, along with environmental variables such as solar irradiance, air temperature, wind speed, wind direction, relative humidity, and angle of...
In this study, the estimation performances of Multiple Linear Regression, Random Forest, and Artificial Neural Network are examined comparatively. For comparison of these data mining techniques, the power production data from a Photovoltaic Module was used in the research. In this study, the model was constituted from seven variables. One of the variables is dependent (power) and the others are independent...
The power production from a photovoltaic module depends on environmental factors such as radiation, air temperature, wind speed and direction and relative humidity. This study aimed to predict some atmospheric indicators which may affect upon the power production from a single axis (East-West) tracking PV module by using Artificial Neural Network architecture. The novelty side of this research is...
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