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This study investigates the use of clustering methodologies as a means of reducing spatio-temporal wind speed data into statistically representative classes of temporal profiles for further processing and interpretation. The clustering methodologies are applied to the high-resolution spatio-temporal, meso-scale renewable energy resource dataset produced for Southern Africa by the Council of Scientific and Industrial Research. This large dataset incorporates thousands of coordinates and represents a challenge from a computational perspective. This dataset can be reduced by applying clustering techniques to classify the temporal wind speed profiles into categories with similar statistical properties. Various clustering algorithms are considered, with the view to compare the performances of these algorithms for large wind resource datasets, namely k-means, partitioning around medoids, the clustering large applications algorithm, agglomerative clustering, the divisive analysis algorithm and fuzzy c-means clustering. Two distance measures are considered, namely the Euclidean distance and Pearson correlation distance. The validation metrics evaluated in the investigation includes the silhouette coefficient, the Calinski-Harabasz index and the Dunn index. Case study results are presented for the Komsberg Renewable Energy Development Zone, located in Western Cape, South Africa. This zone is selected based on the high mean wind speed and large standard deviation exhibited by the temporal wind speed profiles associated with the zone. The effects of seasonal variation in the temporal wind speed profiles are considered by partitioning the input dataset in accordance with the low and high demand seasons defined by the Megaflex Time of Use tariff. The clustered wind resource maps produced by the proposed methodology represent a valuable input dataset for further studies such as siting and the optimal geographical allocation of wind generation capacity to reduce the variability and ramping effects that are inherent to wind energy.

This investigation presents results for the clustering of wind resource data based on statistical Weibull characteristics. The clustering of a chosen geographical area is based on the Weibull and mean wind speed characteristics for each geospatial point for the high energy demand period. The geographically clustered area is chosen from one of the renewable energy development zones, which were identified by the Council of Scientific and Industrial Research. The renewable energy dataset used throughout this study represents the eight renewable energy zones through a meso-scale wind and solar dataset, which spans a 5-year period, at a 15-min temporal resolution. The clustering exercise is aimed at the identification of various geographical areas which best represent a specific independent power producers energy site expectations, while balancing factors such as grid stability and economic and environmental considerations. The study looks into various clustering factors, namely the demand seasons and the energy time of use periods, which correlate to energy production demands for the South African region. The clustering algorithms compared within this study include k-means clustering, the Clustering LARge Applications algorithm, the hierarchical agglomerative algorithm and a model-based clustering algorithm. The initial comparison study yielded the k-means algorithm as the best performing algorithm based on the following internal validation metrics: the Silhouette index, Dunn index and the Calinski-Harabasz index. This clustering method is then subsequently performed on various topical case studies.

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