Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 23; H. 11; S. 1805 - 1815
Hauptverfasser:	Capizzi, G., Napoli, C., Bonanno, F.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.11.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Applied sciences Artificial intelligence Computer science; control theory; systems Connectionism. Neural networks Earth, ocean, space Energy Exact sciences and technology External geophysics Forecasting Humans Inference from stochastic processes; time series analysis Mathematics Meteorology Methodological time series Natural energy Neural Networks (Computer) Neurons photovoltaic (PV) module Photovoltaic conversion prediction Probability and statistics Radiative transfer. Solar radiation recurrent neural networks (RNNs) Sciences and techniques of general use second-generation wavelets Solar Energy Solar radiation Statistics Studies Time series analysis Vectors Wavelet Analysis wavelet theory Wavelet transforms Recurrent neural nets Wind Correlation wavelet theory recurrent neural networks (RNNs) Methodological time series Time series Inverse transformation Neural network Experimental study Mean square error Photovoltaic power plant Wavelet transformation University Humidity prediction Observation data photovoltaic (PV) module Solar radiation Root mean square value Meteorology second-generation wavelets
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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