Analysis of multi-scale chaotic characteristics of wind power based on Hilbert–Huang transform and Hurst analysis

Current status of wind energy forecasting and a hybrid method for hourly predictions

“…The results indicate that multivariate models are more accurate than univariate models and the RNNs outperform the ARIMA models and give 11.5% MAPE for 15 min ahead wind speeds. Another method based on the Hilbert-Huang transform (HHT) and the Hurst analysis for multi-scale wind power forecasting is introduced in [22]. In this, the Hurst analysis is utilized to determine the fractal characteristics of the time-frequency components of the data.…”

Development of a real-time scour monitoring system for bridge safety evaluation

“…A numerical algorithm based on Moment Lyapunov exponent has been applied for the estimation of stochastic dynamic stability of cable-supported bridges [22]. The Lyapunov exponenth as also been used as a reference for identifying the chaotic characteristics of wind power [23]. For the superior adaptability to nonlinear, aperiodic, nonstationary data, the damping spectrum based on the HHT analysis is introduced to evaluate the stability of the complex time-variant scour system.…”

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

“…Such hybrid models used distinct decomposition methods such as Wavelet transform, morphology filters, EMD and many others. The Wavelet transform is not adaptive and adhere to prior knowledge of its mother wavelets and hence somewhat restricts its capability to extract nonlinear and nonstationary components in the data [22,58]. Similarly, the morphology filters have to select the shape and the length of the structural element which has no unified standard and which depends on human experience [104], whereas the EMD method has the great attention of researchers because of its superior performance (even for highly nonlinear and noisy signals) and its easy to understand approach.…”

“…e. The working principle of EMD is empirical without any mathematical/statistical calculations and hence is very easy to understand [28]. f. EMD is fully data driven and a self-adaptive method [22,56,58]. g. EMD is empirical, intuitive, direct and analyzes multi-component signals with predetermined basis functions [13].…”