Feature Based Wind Resource Clustering using an Adapted Phylogenetic Gene Sequencing Approach

There is currently a global push toward a more sustainable energy future, resulting in a rapid increase in the penetration of renewable energy resources into the modern power grid. Increasing the grid integration of variable renewable energy resources calls for robust planning and integration studies. This study explores the novel application of a phylogenetic gene sequence clustering approach as a method to cluster temporospatial wind power profiles that have been defined as categorical feature vectors. The categorical feature vectors embody the statistical characteristics of the temporospatial wind power profiles associated with the South African energy Time of Use tariff periods. The methods used to create phylogenetic hierarchical dendrograms, which depict genetic similarity in organisms, allow room for cross-disciplinary application. In this study, the possible correlation between the molecular phylogenetic comparison of DNA nucleotides in various organisms and the statistical changes between the locationally dependent wind power profiles is explored. The research dataset used for the basis of this study is a mesoscale spatiotemporal wind atlas dataset associated with the South African Renewable Energy Development Zones. The clustered geospatial wind resource map obtained using the proposed methodology is interpreted with reference to the underlying topographical features.