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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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 23; no. 11; pp. 1805 - 1815
Main Authors: Capizzi, G., Napoli, C., Bonanno, F.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.11.2012
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Description
Summary: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 by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2012.2216546