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An Application of Artificial Intelligence Models for Predicting and Controlling Solar Cell Output Power
  • Deogratias NURWAHA
Deogratias NURWAHA
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The study describes the application and comparison of five artificial intelligence methods for PV system output power prediction. General Regression Neural Network (GRNN), Radial Basis Function network (RBF), Group Method of Data Handling (GMDH) network, Multilayer Perceptron neural network (MLP) and Linear Regression (LR). Measured values of temperature (T°C) and irradiance E (Kw/m^2) were used as inputs (independent variables) and PV output power P (Kw) was used as output (dependent variable). Predictive performances have been evaluated using statistical metrics. Comparison of the results provided by the five models has been conducted and commented. It was observed that predictive accuracy depend of the nature of data set used and the optimization parameters of each model. Response surfaces that represent the combined impact of simultaneous variation in temperature and irradiance on PV output power have been illustrated. Curves that showed how close were validation predicted values and actual values have been plotted. Relationship between output power and the two parameters have been illustrated and it was found to be nonlinear. Importance of each ambient parameter contribution to the PV output power has been demonstrated.