Mapping Nominal Values to Numbers by Data Mining Spectral Properties of Leaves

Many data mining algorithms require numeric, preferably normalized (scaled) data i.e. within specific ranges. Agriculture data by default is non-numeric or nominal in nature. Different techniques are available for mapping from nominal to numeric values, and the selection depends on the problem at hand and the mining tool to be used. However, most (if not all) the techniques are based on the statistical properties of the data, and thus miss the intrinsic and natural relationship among the attributes of a plant. We propose a mechanism of performing the mapping from nominal to numeric values (actually ranking) based on the transmittance as well as the statistical properties of the plant. Spectral analysis (using chemical means) is a tedious and time consuming process, thus difficult to repeat, each and every time, for classification of (numerically) unclassified cotton varieties. So a supporting statistical method is proposed based on linear regression curve fitting using normalized nominal attributes. Subsequently a rank is assigned to the variety based on its R 2 value and slope of the plot. This rank thus becomes the numeric equivalent of the nominal alphanumeric name of the variety being considered

Harnessing the Sunlight on Facades - an Approach for Determining Vertical Photovoltaic Potential (Short Paper)

The paper deals with the calculation of the photovoltaic potential of vertical structures. Photovoltaic systems are a core technology for producing renewable energy. As roughly 50% of the population on planet Earth lives in urban environments, the production of renewable energy in urban contexts is of particular interest. As several papers have elaborated on the photovoltaic potential of roofs, this paper focuses on vertical structures. Hence, we present a methodology to extract facades suitable for photovoltaic installation, calculate their southness and percentage of shaded areas. The approach is successfully tested, based on a dataset located in the city of Graz, Styria (Austria). The results show the wall structures of each building, the respective shadow depth, and their score based on a multi-criteria analysis that represents the suitability for the installation of a photovoltaic system