Characterizing Wind Speed Variability in South Africa: A Signal Decomposition-Based Approach

Abstract

Optimizing wind resources is key to the renewable energy transition, especially in regions with significant wind potential like South Africa. Addressing wind speed variability is fundamental to maintaining a consistent energy supply. This study addresses the challenge of quantifying wind speed variability, crucial for efficient wind farm siting and power system stability. By employing ensemble empirical mode decomposition coupled with time-of-use feature extraction, the research methodically decomposes wind speed signals into intrinsic mode functions to capture their fundamental oscillatory behavior. The study focuses on viable locations within a 5×5 km grid, excluding environmentally sensitive areas, to align with sustainable development principles. The k-means clustering algorithm classifies these locations based on wind profile characteristics, with the optimal number of clusters determined using elbow plot and average silhouette width methods. This process unravels intricate patterns of wind dynamics, vital for strategic wind energy infrastructure placement and the management of intermittent wind energy supply. The study's results provide valuable insights into temporal wind dynamics, contributing to the reliability of wind energy in South Africa.