SKIES CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS (ANN) TECHNIQUES

In 2003, the International Commission on Illumination (CIE) adopted 15 standard skies that cover thewhole probable spectrum of usual skies found in the world. Each sky represents a unique distribution.Once the standard sky has been identified, the sky irradiance and outdoor illuminance at any surfacesof interest can be obtained for subsequent investigations and complicated expressions for inclinedsurface models are not required. In Hong Kong where sky obstructions can be very large and ofirregular shapes, sky distribution models that are specified by a given standard sky are moreappropriate for such analyses. Long-term sky luminance data measurement is considered the mostaccurate approach of setting up the database. Alternatively, standard skies can be categorized byvarious climatic parameters of which the selection depends on their availability, accuracy, suitability andfrequency of occurrence at a given location. Artificial neural networks (ANN) represent a powerful toolfor pattern recognition. They learn the relationship between the input elements and the controllableand uncontrollable parameters by studying previous recorded data. This study presents the work onthe standard sky classification using the ANN techniques. Solar and sky data recorded by ourmeasuring station are used for the analysis. The findings showed that sky conditions can be correctlyclassified up to over 90% using the proposed approach.