Nature-Inspired Approach Using Seasonal Comparison of Wind Speed With Spectral and Statistical Analysis

Seasonal analysis of wind speed includes elements of its evaluation and analysis for wind energy production in complex geographical areas. These analyses require wind energy systems to be set up, integrated, operated, and designed according to seasonal differences. Istanbul wind speed data were collected hourly and analyzed seasonally. When the results of the analysis are examined, no significant increase in seasonal transitions was observed, while certain changes were observed between summer and winter. Here, statistical analysis, Weibull distribution function, and signal processing-based PSD analysis for wind speed is performed. In addition, correlation analysis was made between the seasons. Although significant results were obtained in signal-based analyses, results were obtained for seasonal transitions in correlation analyses. Seasonal spectral densities were calculated in the spectral analysis of wind speed data. This study has important implications in terms of extraction of seasonal characteristics of wind speed, resource assessment, operation, investment, and feasibility.

Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections

Sea wind speed forecast is important for meteorological navigation system to keep ships in safe areas. The high volatility and uncertainty of wind make it difficult to accurately forecast multistep wind speed. This paper proposes a new decomposition-based model to forecast hourly sea wind speeds. Because mode mixing affects the accuracy of the empirical mode decomposition- (EMD-) based models, this model uses the variational mode decomposition (VMD) to alleviate this problem. To improve the accuracy of predicting subseries with high nonlinearity, this model uses stacked gate recurrent units (GRU) networks. To alleviate the degradation effect of stacked GRU, this model modifies them by adding residual connections to the deep layers. This model decomposes the nonlinear wind speed data into four subseries with different frequencies adaptively. Each stacked GRU predictor has four layers and the residual connections are added to the last two layers. The predictors have 24 inputs and 3 outputs, and the forecast is an ensemble of five predictors’ outputs. The proposed model can predict wind speed in the next 3 hours according to the past 24 hours’ wind speed data. The experiment results on three different sea areas show that the performance of this model surpasses those of a state-of-the-art model, several benchmarks, and decomposition-based models.

Chaotic Analysis and Prediction of Wind Speed Based on Wavelet Decomposition

Studying the characteristics of wind speed is essential in wind speed prediction. Based on long-term observed wind speed data, fractal dimension analysis of wind speed was first conducted at different scales, and persistence in wind speed was evaluated based on fractal dimensions in this paper. To propose a more accurate model for wind speed prediction, the wavelet decomposition method was applied to separate the high-frequency dynamics of wind speed data from the low-frequency dynamics. Chaotic behaviors were studied for each decomposed component using the largest Lyapunov exponents method. A proposed hybrid prediction method combining wavelet decomposition, a chaotic prediction method and a Kalman filter method was investigated for short-term wind speed prediction. Simulation results showed that the proposed method can significantly improve prediction accuracy.

Influence of the Random Data Sampling in Estimation of Wind Speed Resource: Case Study

In this study, statistical analysis is performed in order to characterize wind speeds distribution according to different samples randomly drawn from wind speed data collected. The purpose of this study is to assess how random sampling influences the estimation quality of the shape (k) and scale (c) parameters of a Weibull distribution function. Five stations were chosen in West Africa for the study, namely: Accra Kotoka, Cotonou Cadjehoun, Kano Mallam Aminu, Lomé Tokoin and Ouagadougou airport. We used the energy factor method (EPF) to compute shape and scale parameters. Statistical indicators used to assess estimation accuracy are the root mean square error (RMSE) and relative percentage error (RPE). Study results show that good accuracy in Weibull parameters and power density estimation is obtained with sampled wind speed data of 30% for Accra, 20% for Cotonou, 80% for Kano, 20% for Lomé, and 20% for Ouagadougou site. This study showed that for wind potential assessing at a site, wind speed data random sampling is sufficient to calculate wind power density. This is very useful in wind energy exploitation development.

Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density

To install a wind energy conversion system to a region, the wind speed characteristics of that region must be identified. The two-parameter Weibull distribution is highly efficient in modeling wind speed characteristics. In this study, the wind speed data of 32 cities in three different regions of Turkey have been comparatively analysed to estimate Weibull distribution function parameters by the use of three well-known methods (Graphical Method (GM), Maximum Likelihood Method (MLM), Justus Moment Method (JMM)) and three new parameter estimation methods (Energy Pattern Factor Method (EPFM), Wind Energy Intensification Method (WEIM), Power Density Method (PD)) which have been proposed in recent years. Three years of hourly wind speed data of the specified regions have been used. The performance metrics of these analyses have been compared using Wind Energy Error (WEE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The results have shown that while the PD method has high model performance, the JMM is closely competitive with the MLM. Besides, the wind energy densities that were estimated by using actual data have been compared with the resulting Weibull distribution. It has been clear that the method that has the closest estimation to the actual values is the PD method.