A Novel Self-Adaptive Wind Speed Prediction Model Considering Atmospheric Motion and Fractal Feature

Jin, Jianyong; Wang, Bin; Yu, Min; Liu, Jiang

doi:10.1109/access.2020.3041533

Cited by 10 publications

(5 citation statements)

References 40 publications

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“…6 represents a comparison study of the IWSP-CSODL model with other models under scenario 1. The experimental values ensured the effectual predictive outcomes of the IWSP-CSODL model with minimal values of MSE, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) [43][44][45][46]. Concerning MSE, the IWSP-CSODL model has offered a lower MSE of 0.5318.…”

Section: Level Ii: Cso-based Hyperparameter Tuning Processmentioning

confidence: 93%

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Surendran¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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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 estimates the wind speed using a hybrid deep learning and hyperparameter optimizer. In the presented IWSP-CSODL model, the prediction process is performed via a convolutional neural network (CNN) based long short-term memory with autoencoder (CBLSTMAE) model. To optimally modify the hyperparameters related to the CBLSTMAE model, the chicken swarm optimization (CSO) algorithm is utilized and thereby reduces the mean square error (MSE). The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios. The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.

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Section: Level Ii: Cso-based Hyperparameter Tuning Processmentioning

confidence: 93%

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Surendran¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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“…In [147], the authors proposed the chaotic PSO algorithm for predicting the mobile location and achieved better location accuracy and faster convergence rate than such algorithms as those of Chan, Taylor, and PSO. Ji Jin et al developed the fractal dimension-based EMD method and GA tuned BPNN model for predicting the wind speed in wind farms by considering the atmospheric motions' fractal feature [152]. The proposed models showed better performance than LSTM, GA tuned BPNN, and ensemble EMD-GA-BPNN.…”

Section: Optimization Algorithms With Ann-based Forecasting Approachesmentioning

confidence: 99%

“…The empirical dynamic model presented in [165] forecast the wind speed for various height levels. At the same time, the fractal dimensional-based self-adaptive model for wind speed predicted atmospheric motion and fractal features [152].…”

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confidence: 92%

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Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Ramadevi

Bingi

2022

Symmetry

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Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed.

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“…Ship movements at sea are primarily influenced by wind and waves. Jin et al [19] has verified the chaotic nature of wind speed time series through an analysis of atmospheric motion, while Xu et al [20] has examined sea clutter time series using phase space reconstruction and fractal methods, concluding that they also exhibit certain chaotic characteristics. Building upon this analysis, this paper aims to investigate whether ship motion attitude time series possess similar chaotic characteristics and explore how to leverage this characteristic for targeted predictions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic chaos unveiled: enhancing ship’s attitude time series prediction through spatiotemporal embedding and improved transformer model

Zhao,

Wang,

Wang

et al. 2024

Meas. Sci. Technol.

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During ship operations at sea, the vessel's attitude undergoes continuous changes due to various factors such as wind, waves, and its own motion. These influences are challenging to mathematically describe, and the changes in attitude are also influenced by multiple interconnected factors. Consequently, accurately predicting the ship's attitude presents significant challenges. Previous studies have demonstrated that phenomena like wind speed and wave patterns exhibit chaotic characteristics when affecting attitude changes. However, research on predicting ship attitudes lacks an exploration of whether chaotic characteristics exist and how they can be described and applied. This paper initially identifies the chaotic characteristics of ship attitude data through phase space reconstruction analysis and provides mathematical representations for them. Based on these identified chaotic characteristics, aTransformer model incorporating feature embedding layers is employed for time series prediction. Finally, a comparison with traditional methods validates the superiority of our proposed approach.

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A Novel Self-Adaptive Wind Speed Prediction Model Considering Atmospheric Motion and Fractal Feature

Cited by 10 publications

References 40 publications

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Dynamic chaos unveiled: enhancing ship’s attitude time series prediction through spatiotemporal embedding and improved transformer model

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