Chaotic characteristic analysis of short-term wind speed time series with different time scales

Tian, Zhongda

doi:10.1080/15567036.2019.1649757

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…This type of behaviors is unstable since it tends to be deterministic at short term but random at long term. Such kind of time series are usually present in many science and engineering fields such as weather forecasting (Tian 2019), financial markets forecasting (Bukhari et al 2020), energy forecasting (Bourdeau et al 2019), intelligent transport and trajectory forecasting (Giuliari et al 2021), etc. Based on the literature (Chandra and Zhang 2012, Montgomery et al 2015, Liu et al 2017, Fischer et al 2018, the five aforementioned behaviors (deterministic, random-walk, nonlinear, long-memory, and chaotic) are the main behaviors encountered in real applications.…”

Section: Chaotic Behaviormentioning

confidence: 99%

What is the best RNN-cell structure for forecasting each time series behavior?

Khaldi¹,

Afia²,

Chiheb³

et al. 2022

Preprint

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It is unquestionable that time series forecasting is of paramount importance in many fields. The most used machine learning models to address time series forecasting tasks are Recurrent Neural Networks (RNNs).Typically, those models are built using one of the three most popular cells, ELMAN, Long-Short Term Memory (LSTM), or Gated Recurrent Unit (GRU) cells, each cell has a different structure and implies a different computational cost. However, it is not clear why and when to use each RNN-cell structure.Actually, there is no comprehensive characterization of all the possible time series behaviors and no guidance on what RNN cell structure is the most suitable for each behavior. The objective of this study is two-fold: it presents a comprehensive taxonomy of all-time series behaviors (deterministic, random-walk, nonlinear, long-memory, and chaotic), and provides insights into the best RNN cell structure for each time series behavior. We conducted two experiments: (1) The first experiment evaluates and analyzes the role of each component in the LSTM-Vanilla cell by creating 11 variants based on one alteration in its basic architecture (removing, adding, or substituting one cell component). ( 2) The second experiment evaluates and analyzes the performance of 20 possible RNN-cell structures. Our results showed that the MGU-SLIM3 cell is the most recommended for deterministic and nonlinear behaviors, the MGU-SLIM2 cell is the most suitable for random-walk behavior, FB1 cell is advocated for long-memory behavior, and LSTM-SLIM1 for chaotic behavior.

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Section: Chaotic Behaviormentioning

confidence: 99%

What is the best RNN-cell structure for forecasting each time series behavior?

Khaldi¹,

Afia²,

Chiheb³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Time delay and embedding dimension are two important parameters for reconstructing the phase space of the time series, which can be calculated by using different approaches. e autocorrelation method [27], multiple autocorrelation method [28], and mutual information method [29] are commonly used to obtain time delay τ. e G-P method [13], false nearest neighbor method [14,27], and Cao method [29,30] are widely applied in gaining the embedding dimension. Some researchers assumed that time delay and embedding dimension are not independent of each other, so they utilized the C-C method [15][16][17][18][19] to acquire time delay and embedding dimension synchronously.…”

Section: Identification Of Chaotic Characteristicsmentioning

confidence: 99%

“…Time delay and embedding dimension are two important parameters for reconstructing the phase space of the time series, which can be calculated by using different approaches. Tian et al [13] used the C-C algorithm to obtain delay time and calculated the embedding dimension based on the G-P algorithm for a short-term wind speed time series. Based on the condition monitoring data of a methane compressor, Niu and Yang [14] gained the reconstruction parameters (delay time and embedding dimension) by utilizing the C-C method and false nearest neighbor method, respectively, to predict the degradation trend.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Dynamic Uncertainty of Rolling Bearing Vibration Performance

Liang

Xia

Chang³

2019

Mathematical Problems in Engineering

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The variation trend, failure trajectory, probability distribution, and other information vary with time and working conditions for rolling bearing vibration performance, which makes the evaluation and prediction of the evolution process difficult for the performance reliability. In view of this, the chaos theory, grey bootstrap method, and maximum entropy method were effectively fused to propose a mathematical model for the dynamic uncertainty evaluation of rolling bearing vibration performance. After reconstructing the phase space of the vibration performance time series, four local prediction methods were applied to predict the vibration values of bearings to verify the effectiveness and validity of chaos theory. The estimated true value and estimated interval were calculated using the grey bootstrap method (GBM) and maximum entropy method. Finally, the validity of the proposed model was verified by comparing the probability that the original data fall into the estimated interval with the given confidence level. The experimental results show that the proposed method can effectively predict the variation trend and failure trajectory of the vibration performance time series so as to realize the dynamic monitoring of the evolution process for rolling bearing vibration performance online.

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“…Because of the instability of wind itself, wind power generation is volatile and intermittent, which will cause serious difficulties in wind power dispatch (Wang et al, 2020). Therefore, accurate prediction of wind power can effectively alleviate the strong uncertainty brought by large-scale wind power grid connection to power system operation, which has important theoretical and practical application value (Li et al, 2020a; Tian, 2019; Tian et al, 2019). According to the characteristics of time scale, research object, dependent data, prediction model, and different implementation methods, wind power prediction methods can have many classification systems, but no matter what classification system is divided, there is no unified standard in academia and industry.…”

Section: Introductionmentioning

confidence: 99%

A state-of-the-art review on wind power deterministic prediction

Tian

2020

Wind Engineering

Self Cite

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With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

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Chaotic characteristic analysis of short-term wind speed time series with different time scales

Cited by 8 publications

References 32 publications

What is the best RNN-cell structure for forecasting each time series behavior?

What is the best RNN-cell structure for forecasting each time series behavior?

Evaluation of Dynamic Uncertainty of Rolling Bearing Vibration Performance

A state-of-the-art review on wind power deterministic prediction

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