Use of neural networks for the creation of hourly global and diffuse solar irradiance data at representative locations in Greece

In this work, a new approach is tested by applying neural networks treatment to meteorological time-series data sets, recorded during 1991–2000 at certain Greek locations, in order to create fully appropriate solar data information. Neural networks, in this case, are used for creating missing mean, maximum and minimum global and diffuse solar irradiance hourly data, when educated with other known meteorological time-series hourly values. For this purpose, hourly data of air temperature, relative humidity, sunshine duration, clouds’ octals, as well as local latitude are used with regard to these sites. Neural networks’ education process outputs are checked against known hourly values of solar irradiance, based upon the mentioned meteorological hourly raw data necessary for this action recorded at the National Observatory of Athens, the actinometric station at the Technological Education Institute (TEI) of Piraeus, and six other locations. Selection of these sites is representative of the climatic conditions in Greece, from north to south and east to west. Following the same scheme, the produced hourly global and diffuse mean hourly solar irradiance values are in a very good agreement (p<0.01) with actual measurements.