Sensitivity analysis of climatic parameters for sky classification

Climatic variables are frequently used as weighting factors to indicate the degree of clearness for interpreting sky patterns. However, such important parameters are not always widely available and their criteria to define a sky condition are not clear-cut. In addition, certain variables may be more effective than the others in terms of sky identification. This paper studies the capability of various daylight parameters, namely zenith luminance, global, direct-beam and sky-diffuse illuminance, and solar altitude for categorizing the 15 International Commission on Illumination (CIE) standard skies. A new form of artificial neural networks called probabilistic neural network (PNN) which is a powerful technique for pattern recognition was used for the analysis. The findings suggested that the PNN is an appropriate tool when a number of climatic parameters of various criteria for differentiating sky standards are employed, and the ratio of zenith luminance to diffuse illuminance ( L z / D v ) and solar altitude ( α s ) are respectively the most and the least significant input parameters for discriminating between the 15 CIE skies.