Review solar prediction iea 07-06
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A REVIEW OF SOLAR IRRADIANCE PREDICTION TECHNIQUES Martín, L.; Zarzalejo, L. F.; Polo, J.; Espinar, B. & Ramírez, L SOLAR RESOURCE KNOWLEDGE MANAGEMENT TASK No. 36 IEA Solar Heating & Cooling Programme CIEMAT WORKING GROUP (SPAIN) SUBTASK A: Standard Qualification For Solar Resource Products 6-7 July 2006 Denver, Colorado
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Transcript of Review solar prediction iea 07-06
A REVIEW OF SOLAR IRRADIANCE PREDICTION TECHNIQUES
Martín, L.; Zarzalejo, L. F.; Polo, J.; Espinar, B. & Ramírez, L
SOLAR RESOURCE KNOWLEDGE MANAGEMENT
TASK No. 36IEA Solar Heating & Cooling Programme
CIEMAT WORKING GROUP (SPAIN)
SUBTASK A: Standard Qualification For Solar Resource Products
6-7 July 2006 Denver, Colorado
SOLAR PREDICTION OVERVIEW
• Solar Energy:– Dynamic in the atmosphere the oceans and in general life on earth.
– Solar water heating, water detoxification, water desalinization, electric power energy
generation from solar thermal power and photovoltaic energy, agricultural applications….• Need to characterize and predict incoming solar radiation to be used
as a energetic resource.
• Prediction General Techniques1. Numerical Weather Predictions Models
2. Statistical Prediction
• Forecasting Horizon– Nowcasting– Short Term– Medium Term– Long Term
MOS SOLAR PREDICTION – SHORT TERM
Differents Works from 80s:John S. Jensenius& Gerald F. Cotton, 1981:
The developmentand testing of automated Solar energy forecasts based on the model output statistics (MOS) technique
1st Workshop On Terrestrial SolarResource Forecasting and
on the Use on Satellites for Terrestrial Solar Resource Assesssment, Newark, 1981, Am. Sol. En. Soc.
New appraches using sky cover product from wheather prediction centers:
( )clear sky
GHIg SK
GHI
SATELLITE SOLAR PREDICTION
Annette Hammer, Detlev Heinemann, Carster Hoyer, Elke Lorenz. Satellite based short-term
forecasting of solar irradiance - comparison of methods and error analysis. 2000.
SIGNAL ANALISYS AND ARTIFICIAL INTELIGENT APPROACHES
Cao S, Cao J. Forecast of solar irradiance using recurrent neural networks combined with wavelet analysis. Applied Thermal Engineering 2005 Feb;25(2-3):161-72.
Signal Analysis Time-Frecuency (Scale) with Wavelet Transform
Prediction with Artificial Neural Networks
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Discrete Wavelet Transform:Signals Filtered:
High Frecuency (Detail)Low Frecuency (Aproximation)
FUTURE WORKS
• Wavelet analysis and NN with Normalized data (Kt).• Use other NN architectures like Self-Organized Features
Maps (SOFMs).• Use network surface irradiance data forecasted from NWP
from European Centre Medium Weather Forecasting (ECMWF) as a new parameter in NN.
• Wavelet and temporal series technique.• Motion estimation with segmentation techniques in
satellite images.• Med-Long Term Prediction: EOF Analysis analysis to
relate different atmospheric oscillation patterns, NAO (North Atlantic Oscillation), ENSO (El Niño-Southern Oscillation),… with expected solar irradiance.
