The total solar radiation time series simulation in Athens, using neural networks

The present study describes a neural network approach for modeling and making short-term predictions on the total solar radiation time series. The future hourly values of total solar radiation for several years are predicted, by extracting knowledge from their past values, using feedforward backpropagation neural networks. The results are tested using various sets of non training measurements, the findings are very encouraging and the model is found able to simulate the future values of total solar radiation time series based on their past values. "Multilag" output predictions are per-formed using the predicted values to the input database in order to model future total solar radiation values with sufficient accuracy. Furthermore, an autoregressive model is developed fur analysing and representing the total solar radiation time series. The predicted values of solar radiation are compared with the observed data series and it was found that the neural network approach leads to better predictions than the AR model.