Abstract
The stochastic production of renewable energy sources puts increased demands on power grids worldwide. Neurocomputing methods can be used for the forecast of electric energy production of renewable resources and contribute to the reliability of energy systems. This study compares two neurocomputing methods as predictors of a selected photovoltaic power plant in the Czech Republic that meets the real world criterion of high output variance and relatively large installed power. © 2012 Springer-Verlag.
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CITATION STYLE
Prokop, L., Mišák, S., Novosád, T., Krömer, P., Platoš, J., & Snášel, V. (2012). Photovoltaic power plant output estimation by neural networks and fuzzy inference. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7435 LNCS, pp. 810–817). https://doi.org/10.1007/978-3-642-32639-4_96
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