The total solar radiation time series simulation in Athens, using neural networks |
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| Authors: | G Mihalakakou M Santamouris D N Asimakopoulos |
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| Institution: | (1) Laboratory of Meteorology, Division of Applied Physics, Department of Physics, University of Athens, Greece, GR |
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| Abstract: | Summary 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. “Multi-lag” output predictions are performed 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
for 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.
Received November 22, 1999 Revised February 17, 2000 |
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