Clustering of Wind Resource Weibull Characteristics on the South African Renewable Energy Development Zones

Abstract

This paper presents the results of an investigation done to cluster the wind speed profiles associated with the South African Renewable Energy Development Zones using the associated Weibull distribution characteristics, together with the mean wind speed. The study uses a meso-scale wind resource dataset produced by the Council for Scientific and Industrial Research. Various clustering methods are explored, namely k-means clustering, the clustering large applications algorithm, the hierarchical agglomerative algorithm and a model-based clustering algorithm. Results are presented for each of the clustering algorithms for the Springbok renewable energy development zone for the high demand season wind speed profiles. These results include the non-overlapping clusters obtained, the Weibull distribution of the average profile associated with each cluster, the mean daily wind speed associated with each cluster and final analysis with an associated geographical cluster map. Clustering performance metrics, including the average silhouette width, Dunn index, the average intra-cluster distance, connectivity and the Calinski-Harabasz index are presented and interpreted. The clustering performance metrics achieved indicate that the k-means algorithm performs best when clustering on the Weibull distribution characteristics.