Abstract
This work presents and compare six short-term forecasting methods for hourly aggregated solar generation. The methods forecast one day ahead hourly values of Spanish solar generation. Three of the models are based on MLP network and the other three are based on NARX. The two different types of NN use to forecast the same NWP data, comprising solar radiation, solar irradiation and the cloudiness index weighted with the installed solar power for the whole country. In addition of the NWP data the models are fed with the aggregated solar energy generation in hourly step given by the System Operator.
The results of the two types of NN are compared and discussed in the conclusions as much as the error variability along the day hours. The results obtained by the six methods are evaluated, concluding that the most accurate result is the one given by the developed NARX irradiance forecast method; achieving the lowest one day-ahead Mean Average Daily Error of 16.64%.
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Perez-Mora, N., Canals, V., Martinez-Moll, V. (2015). Short-Term Spanish Aggregated Solar Energy Forecast. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_26
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DOI: https://doi.org/10.1007/978-3-319-19222-2_26
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