Improved Generation of Hourly Solar Irradiation Artificial Series
Using Neural Networks
International Conference on Engineering Applications of Neural Networks
(EANN'99)
Warsaw (Poland).
September 13-15, 1999.
ABSTRACT
This paper presents a new neural network approach for the generation of
synthetic hourly irradiation series, a relevant problem in the photo-voltaic
field. The neural model employed is the well known Multi-Layer Perceptron
(MLP) paradigm, in a feedback architecture, using a record of historical
values for the supervised network training. The method is based on the MLP
ability to extract, from a sufficiently general training set, the existing
relationships between variables whose interdependence is unknown a priori.
Simulation results are compared to other methods, and show that the generated
values follow the average tendency of the real values. Though the method has
been developed using data values from Madrid, it can be generalised to any
location. Even more, the proposed methodology is of general applicability to
the estimation of highly complex temporal series.
|
Related files:
|
![[Paper (PDF)]](pdficon.gif) |
eann99.pdf |
Paper (Adobe Acrobat Portable Document Format PDF) |
![[Poster (PDF)]](pproject.gif) |
eann99pp.pdf |
Presentation Slides (PDF) |
|
|