A Neural Network Approach for Generating Solar Irradiation
Artificial Series
International Work-Conference on Artificial and Natural Neural Networks
(IWANN'99)
Alicante (Spain).
June 2-4, 1999.
ABSTRACT
In this paper a relevant problem in the photovoltaic solar energy field is
considered: the generation of artificial series of hourly solar irradiation.
The proposed methodology artificially generates series following the average
tendency of the hourly radiation series kt in a given
place. This is obtained by making use of a set of historical values of this
series in such place (for training purposes) as well as the daily clarity
index KT of the year to be generated.
This information is employed for the supervised training
of a proposed neural network model. The neural model employs a well known
paradigm, called Multilayer Perceptron (MLP), in a feedback architecture.
The generation method is based on the MLP ability to extract, from a
suficiently general training set, the existing relationships between
variables whose interdependence is unknown a priori. This way, the presented
design methodology can implicitly include all the available information.
Simulation results show the good performance of the irradiation series
generator, and the general applicability of this methodology in the estimation
of highly complex temporal series.
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Presentation Poster (PDF) |
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