Diverse dynamical characteristics across the frequency spectrum of wind speed fluctuations

• Published in: Renewable energy 2018-04, Vol.119, p.540-550
• Main Authors: Drisya, G.V., Asokan, K., Kumar, K. Satheesh
• Format: Article
• Language: English
• Subjects: Chaos; Linear first order method; Wavelet decomposition; Wind speed
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2017.12.024

Wind speed oscillations are known to exhibit varying characteristics at different time scales. Our recent analysis has shown that a collection of autoregressive models fitted separately on the frequency components of wind speed data can significantly increase the prediction accuracy. In this paper, we report the results of the investigation of dynamical behaviour across a broad frequency spectrum of wind speed measurements. The results show the existence of diverse characteristics such as stochastic, deterministic and chaotic behaviour apart from the variation of the dimensionality of underlying dynamics as well as the degree of fluctuations. It is also demonstrated that a cluster of deterministic models built upon separate frequency components of a wind speed time series can enhance the prediction accuracy by as much as 80%, on the average, consistently for predictions up to 12 h. The comparison shows the definite advantage of deterministic prediction models over autoregressive models. The f-index introduced in this paper to measure the fluctuations of wind speed over a period indicates that the observed seasonal variations of prediction errors can be correlated with changes in the f-index of the component series contributed mostly by the lower scales of decomposition. •Diverse dynamics across the frequency spectrum of wind speed fluctuations.•Components wise modelling improves deterministic prediction accuracy up to 80%.•Definite advantage of deterministic prediction models over autoregressive models.•Fluctuation index shows correlation with prediction accuracy and seasonal variations.