Yongxin Liu¹, Puyu Zhao², Jianxin Xu², Xiaokai Meng¹, Hong Yang¹, Bo He^2,*

Structural Durability & Health Monitoring, Vol.17, No.6, pp. 521-539, 2023, DOI:10.32604/sdhm.2023.029850

Abstract Numerical simulation of the spatial wind field plays a very important role in the study of wind-induced response law of transmission tower structures. A reasonable construction of a numerical simulation method of the wind field is conducive to the study of wind-induced response law under the action of an actual wind field. Currently, many research studies rely on simulating spatial wind fields as Gaussian wind, often overlooking the basic non-Gaussian characteristics. This paper aims to provide a comprehensive overview of the historical development and current state of spatial wind field simulations, along with a detailed introduction to standard simulation methods.… More > Graphic Abstract

Neelam Mughees^1,2, Mujtaba Hussain Jaffery¹, Abdullah Mughees³, Anam Mughees⁴, Krzysztof Ejsmont^5,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564

Abstract Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050. However, they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions. In microgrids, smart energy management systems, such as integrated demand response programs, are permanently established on a step-ahead basis, which means that accurate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids. With this in mind, a novel “bidirectional long short-term memory network” (Bi-LSTM)-based, deep stacked, sequence-to-sequence autoencoder (S2SAE) forecasting model… More >

R. Surendran^1,*, Youseef Alotaibi², Ahmad F. Subahi³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3371-3386, 2023, DOI:10.32604/csse.2023.034465

Abstract High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model… More >

Shafiqur Rehman^1,*, Hilal H. Nuha², Ali Al Shaikhi³, Satria Akbar², Mohamed Mohandes^1,3

Energy Engineering, Vol.120, No.4, pp. 775-789, 2023, DOI:10.32604/ee.2023.026185

Abstract An accurate vertical wind speed (WS) data estimation is required to determine the potential for wind farm installation. In general, the vertical extrapolation of WS at different heights must consider different parameters from different locations, such as wind shear coefficient, roughness length, and atmospheric conditions. The novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks (RNN) model to estimate WS at different heights using measurements from lower heights. The first optimization of the RNN is performed to minimize a differentiable cost function, namely, mean squared error (MSE), using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Secondly,… More >

Musaed Alrashidi^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1073-1088, 2023, DOI:10.32604/cmc.2023.036170

Abstract Harvesting the power coming from the wind provides a green and environmentally friendly approach to producing electricity. To facilitate the ongoing advancement in wind energy applications, deep knowledge about wind regime behavior is essential. Wind speed is typically characterized by a statistical distribution, and the two-parameters Weibull distribution has shown its ability to represent wind speeds worldwide. Estimation of Weibull parameters, namely scale and shape parameters, is vital to describe the observed wind speeds data accurately. Yet, it is still a challenging task. Several numerical estimation approaches have been used by researchers to obtain c and k. However, utilizing such… More >

Siva Sankari Subbiah¹, Senthil Kumar Paramasivan^2,*, Karmel Arockiasamy³, Saminathan Senthivel⁴, Muthamilselvan Thangavel²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3829-3844, 2023, DOI:10.32604/iasc.2023.030480

Abstract Wind speed forecasting is important for wind energy forecasting. In the modern era, the increase in energy demand can be managed effectively by forecasting the wind speed accurately. The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty, the curse of dimensionality, overfitting and non-linearity issues. The curse of dimensionality and overfitting issues are handled by using Boruta feature selection. The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory (Bi-LSTM). In this paper, Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed… More >

Wenzheng Yu¹, Yang Gao¹, Zhengyu Yuan¹, Xin Yao^1,*, Mingxuan Zhu¹, Hanxiaoya Zhang²

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 563-576, 2022, DOI:10.32604/cmc.2022.027008

Abstract

Poisson-Gumbel joint distribution model uses maximum wind speed corresponding to multiple typhoons to construct sample sequence. Thresholds are usually used to filter sample sequences to make them more consistent with Poisson distribution. However, few studies have discussed the threshold setting and its impact on Poisson-Gumbel joint distribution model. In this study, a sample sequence based on the data of Qinzhou meteorological station from 2005 to 2018 were constructed. We set 0%, 5%, 10%, 20% and 30% gradient thresholds. Then, we analyzed the influence of threshold change on the calculation results of maximum wind speed in different return periods. The results… More >

Aodi Fu¹, Mingxuan Zhu², Wenzheng Yu^1,*, Xin Yao¹, Hanxiaoya Zhang³

Journal on Big Data, Vol.4, No.1, pp. 77-86, 2022, DOI:10.32604/jbd.2022.027477

Abstract In 2014, Typhoon Rammasun invaded Qinzhou, Guangxi, causing damage to the wind tower sensor at 80 m in Qinzhou. In order to restore the wind speed at 80 m at that time, this paper was based on the hourly average wind speed data of the wind tower and meteorological station from 2017–2019, and constructed the wind speed related model of Meteorological Station and the wind measuring tower in Qinzhou, Moreover, this paper Based on the hourly average wind speed data of Qinzhou Meteorological Station in 2014, Restored the hourly average wind speed of the anemometer tower during Rammasun landfalled. The results showed… More >

Yingnan Zhao^1,*, Peiyuan Ji¹, Fei Chen¹, Guanlan Ji¹, Sunil Kumar Jha²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1001-1016, 2022, DOI:10.32604/iasc.2022.027710

Abstract This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and-excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of each component. Contrasting experiments for… More >

Yingnan Zhao^1,*, Guanlan Ji¹, Fei Chen¹, Peiyuan Ji¹, Yi Cao²

Computer Systems Science and Engineering, Vol.43, No.2, pp. 719-735, 2022, DOI:10.32604/csse.2022.027288

Abstract Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers. This paper proposes a new variational mode decomposition (VMD)-attention-based spatio-temporal network (VASTN) method that takes advantage of both temporal and spatial correlations of wind speed. First, VASTN is a hybrid wind speed prediction model that combines VMD, squeeze-and-excitation network (SENet), and attention mechanism (AM)-based bidirectional long short-term memory (BiLSTM). VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions (IMF). Then, to extract the spatial features at the bottom of the model, each IMF employs an improved convolutional… More >