Prediction of total AC power output from a grid-photovoltaic system using multi-model ANN

This paper presents the prediction of total AC power output from a grid-photovoltaic system using multimodel artificial neural network (ANN). In this study, three-layer feedforward Artificial Neural Network (ANN) model for the prediction of total AC power output from a grid-photovoltaic system has been considered. The multi-model was configured from three ANN models considering different sets of ANN inputs. The first model utilizes solar radiation and ambient temperature as its inputs while the second model uses solar radiation and wind speed as its inputs. The third model uses solar radiation, ambient temperature and wind speed as its inputs. Nevertheless, all the three models employ similar type of output which is the total AC power produced from the grid-PV system. Data filtering process has been introduced to select the quality data patterns to be used in training process, making only the informative features are available. Thus, the regression analysis and root mean square error (RMSE) performance of each model could be enhanced. After the training process is completed, the testing process is performed to decide whether the training process should be repeated or stopped. Besides selecting the best prediction model, this study also exhibits some of the experimental results which illustrate the effectiveness of the data filtering in predicting the total AC power output from a grid-PV system. Fully trained ANN model should be later able to predict the AC power output from a set of un-seen data patterns.