Hourly solar radiation forecasting through neural networks and model trees
Hamilton, Cameron Reid
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Solar radiation forecasting models were developed in order to determine the specific times during a given day that solar panels could be relied upon to produce energy sufficient to meet the demand of the energy provider, Georgia Power. These models, which consisted of multilayer perceptrons (MLP), model averaged neural networks (MANN) and alternating model trees (AMT), were constructed to forecast solar radiation an hour into the future, given 2003-2012 solar radiation data from the Griffin, Georgia weather station for training and 2013 data for testing. A literature review of the most prominent hourly solar radiation models was performed and normalized root mean square error was calculated for each. The results demonstrate that MANN and AMT models outperform or parallel the highest-performing models within the literature. MANN and AMT are thus promising forecasting models that may be further improved by forming an ensemble of these models with the top performing within the literature.