Integrating the Spatial Autocorrelation Structure of Irregularly Distributed Wind Measurement Stations to Model Wind Patterns Using R

Wind behavior in Sri Lanka, mainly exposed to two major monsoon seasons, is complex to model spatially. The acute changes in the topography further make it difficult to map the wind patterns. Irregularly distributed wind measurements are the main source of data available to interpret and model this dynamic geographic phenomenon. Sri Lanka is in need of an accurate and timely wind map in order to support wind energy resource planning and to enable weather prediction systems to be more accurate. Strong and continuous winds in the southern part of the island encourage wind harvesting for energy. Wind energy is arguably the most affordable per megawatt-hour renewable energy source, and it is growing nearly as quickly as conventional energy generation techniques in the country. The objective of this paper is twofold. First is to use the spatial autocorrelation structure of 23 data points using diurnal data measured every 10 minutes on an irregularly distributed grid to construct continuous wind speed fields for the 4 monsoon seasons. This has been done for the year 2015 as a reference year, where a complete set of data was acquired. Secondly, it was intended to provide the reader how R opensource with advanced statistical modelling capabilities and graphics could be utilized conveniently for vague geographic phenomena analysis. The spatial autocorrelation of the wind speed and the direction was determined in the beginning by using the semi-variograms. Candidate semi-variogram models, especially spherical and exponential models, were tested to be more suitable for the observations. Ordinary Kriging, which uses the samples in the local neighborhood to avoid the mean estimation, was used to perform the spatial interpolations Kriging. For each month of the year 2015, four weekly wind maps were generated, both for the direction and speed. These 48 wind maps were further analyzed for the wind patterns. The changes in the morning and evening wind patterns, and the significant wind pattern changes along the coastal areas and the central highlands are extracted and presented in the study. The generated maps could be useful in identifying potential areas for wind farming. By feeding the data in a time series to the presented model, it would be possible to deduce wind pattern changes in Sri Lanka on a yearly basis. As a whole, the paper presents a reasonable model that could be used to model a diverse set of vague phenomena with an understanding of the uncertainties involved.