Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting
Aymen Chaouachi, Rashad M. Kamel, and Ken Nagasaka
Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Nagasaka Laboratory, 2-24-16, Nakamachi, Koganei, Tokyo 184-8588, Japan
This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks.
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