Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function

Liu, Xin; Zhou, Jun; Qian, Huimin

doi:10.1016/j.epsr.2020.107011

Cited by 49 publications

(10 citation statements)

References 27 publications

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“…To give full play to the advantages of various deep learning models, some researchers combine RNN and other models to improve the performance of driving behavior recognition. For example, Mafeni et al [99] jointly applied BI-LSTM and Incep-tionV3 CNNs to construct a distracted driving model (as shown in Figure 9) and identified distracted driving behaviors through driving posture pictures, with an accuracy of 93.1%. Wollmer et al [33] used RNN combined with random forest FR to identify distracted driving behavior, and the accuracy reached 95%.…”

Section: Recurrent Neuralmentioning

confidence: 99%

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Zhao

Zhong

Zhi-jun

et al. 2022

Journal of Advanced Transportation

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The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

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Section: Recurrent Neuralmentioning

confidence: 99%

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Zhao

Zhong

Zhi-jun

et al. 2022

Journal of Advanced Transportation

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“…In theory, the discrete Volterra functional model can accurately predict the nonlinear time series. When the Volterra filter is used to predict the time series, because it is difficult for the functional model to solve the higher-order kernel function, the second-order or third-order truncation form of the Volterra functional model will be used in general, but this will greatly reduce the prediction accuracy and performance of the model [19][20][21]. In addition, the Volterra functional model has limited memory ability.…”

Section: Volterra Functional Model Of Nonlinear Systemmentioning

confidence: 99%

Air Combat Maneuver Trajectory Prediction Model of Target Based on Chaotic Theory and IGA-VNN

Zhang

Kou

et al. 2020

Mathematical Problems in Engineering

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Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and threat assessment. Aiming at the problem of low prediction accuracy in traditional trajectory prediction methods, combined with the chaotic characteristics of the target maneuver trajectory time series, a target maneuver trajectory prediction model based on chaotic theory and improved genetic algorithm-Volterra neural network (IGA-VNN) model is proposed, mathematically deducing and analyzing the consistency between Volterra functional model and back propagation (BP) neural network in structure. Firstly, the C-C method is used to reconstruct the phase space of the target trajectory time series, and the maximum Lyapunov exponent of the time series of the target maneuver trajectory is calculated. It is proved that the time series of the target maneuver trajectory has chaotic characteristics, so the chaotic method can be used to predict the target trajectory time series. Then, the practicable Volterra functional model and BP neural network are combined together, learning the advantages of both and overcoming the difficulty in obtaining the high-order kernel function of the Volterra functional model. At the same time, an adaptive crossover mutation operator and a combination mutation operator based on the difference degree of gene segments are proposed to improve the traditional genetic algorithm; the improved genetic algorithm is used to optimize BP neural network, and the optimal initial weights and thresholds are obtained. Finally, the IGA-VNN model of chaotic time series is applied to the prediction of target maneuver trajectory time series, and the experimental results show that its estimated performance is obviously superior to other prediction algorithms.

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“…(8) In recent years, the deep learning model has been developed deeply, and it is also widely used in the prediction of wind power. These deep learning models include recurrent neural network (Liu et al, 2021;Wang et al, 2021), long short-term memory (Dinler, 2021;Ko et al, 2021), deep belief network (Habib et al, 2020;Wang et al, 2018b), and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the deep learning model has been developed deeply, and it is also widely used in the prediction of wind power. These deep learning models include recurrent neural network (Liu et al, 2021; Wang et al, 2021), long short-term memory (Dinler, 2021; Ko et al, 2021), deep belief network (Habib et al, 2020; Wang et al, 2018b), and so forth. Compared with traditional machine learning algorithms, deep learning model emphasizes learning from massive data, which can solve the problems of high dimension, clutter, and high noise in massive data that traditional machine learning algorithms are difficult to deal with.…”

Section: Introductionmentioning

confidence: 99%

Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm

Tian

2021

Transactions of the Institute of Measurement and Control

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In recent years, short-term wind power forecasting has proved to be an effective technology, which can promote the development of industrial informatization and play an important role in solving the control and utilization problems of renewable energy system. However, the application of short-term wind power prediction needs to deal with a large number of data to avoid the instability of forecasting, which is facing more and more difficulties. In order to solve this problem, this paper proposes a novel prediction approach based on kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm. Short-term wind power generation is affected by many factors. The original multi-dimensional input variables are pre-processed by kernel principal component analysis to determine the principal components that affect wind power. The dimension of principal component is less than the original input data, which reduces the complexity of modeling. The convergence and stability of the echo state network can be improved by using the principal component of the input variable. The advantage is to reduce the input variables, eliminate the correlation between the input variables, and improve the prediction performance of the prediction model. Furthermore, an improved particle swarm optimization algorithm is proposed to optimize the dynamic reservoir parameters of echo state network. Compared with other state-of-the-art prediction models, the case studies show that the proposed approach has good prediction performance for actual wind power data.

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Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function

Cited by 49 publications

References 27 publications

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Air Combat Maneuver Trajectory Prediction Model of Target Based on Chaotic Theory and IGA-VNN

Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm

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