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Energies 2017, 10(7), 1003; doi:10.3390/en10071003

Prediction in Photovoltaic Power by Neural Networks

1
Department of Information Engineering, Electronics and Telecommunications, University of Rome “La Sapienza”, via Eudossiana, 18, Rome 00184, Italy
2
Electrical Engineering Division of Department of Astronautical, Electrical and Energy Engineering, University of Rome “La Sapienza”, via Eudossiana, 18, Rome 00184, Italy
Series for the Prediction in Photovoltaic Power Plants. In Proceedings of the 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy, 7–10 June 2016.
*
Author to whom correspondence should be addressed.
Academic Editor: Gabriele Grandi
Received: 11 April 2017 / Revised: 11 July 2017 / Accepted: 12 July 2017 / Published: 15 July 2017
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Abstract

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches. View Full-Text
Keywords: embedding technique; power forecasting; photovoltaic power plant; neural and fuzzy neural network embedding technique; power forecasting; photovoltaic power plant; neural and fuzzy neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Rosato, A.; Altilio, R.; Araneo, R.; Panella, M. Prediction in Photovoltaic Power by Neural Networks. Energies 2017, 10, 1003.

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