Preprint Article Version 1 This version is not peer-reviewed

Solar Power Interpolation and Analysis Using Spatial Autocorrelation

Version 1 : Received: 6 December 2018 / Approved: 7 December 2018 / Online: 7 December 2018 (03:55:55 CET)

How to cite: Gillani, S.M.A. Solar Power Interpolation and Analysis Using Spatial Autocorrelation. Preprints 2018, 2018120091 Gillani, S.M.A. Solar Power Interpolation and Analysis Using Spatial Autocorrelation. Preprints 2018, 2018120091

Abstract

To reduce solar power production invariance, it is critical to study varying patterns of power production in the concerned region. Analyzing the patterns of past power production trends can help simulate power production scenarios for future. The current study area is around Amsterdam, located in Netherlands. PVoutput.org website is used to mine 6 months of solar power production data for 120 stations around Amsterdam city. FME Workbench software is used to actively fetch the data from the mentioned website and manage in a MySQL database. Solar attenuation maps created using ArcGIS, helped to graphically visualize the variations in solar power production at different times and locations. Further, spatial autocorrelation is checked between the stations using semi-variograms in geostatistical tool of ArcMap. This feature allows to check whether the stations located close to each other are more correlated to each other rather than stations which are far apart. The statistical data analysis of power production can aid solar power production companies to better interpolate and predict solar power in advance for the concerned study region.

Subject Areas

solar power interpolation; solar power attenuation; spatial autocorrelation; semi-variograms; geosatistics

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