Wind power forecasting using attention-based gated recurrent unit network

Niu, Zhewen; Yu, Zeyuan; Tang, Wenhu; Wu, Q. H.; Reformat, Marek

doi:10.1016/j.energy.2020.117081

Cited by 236 publications

(59 citation statements)

References 39 publications

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“…For instance, the authors used TCN, GRU, ENN, BRNN, and LSTM architectures to forecast information related to wind in. 134,136,137,146,147,149,155 Water quality and demand were also predicted by using TCN and ENN in Refs. 140,153 An application of LSTMbased neural networks for correlated time series prediction was also proposed by Wan et al 143 Further, carbon dioxide emissions, 139 flood, 143 or NH 3 concentration for swine house 199 were also predicted by using deep-learning techniques, in particular ENN.…”

Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyperparameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

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Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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“…In recent years, long short-term memory (LSTM) and gated recurrent unit (GRU), two popular variants of RNNs, have overcome the limited shortcoming of discovering long-term dependencies to some extent, and have achieved success in various applications [ 34 , 35 , 36 ]. To further solve time series prediction problems, some researchers have introduced attention mechanisms into deep neural networks [ 37 , 38 , 39 ]. Inspired by [ 40 ], a temporal pattern attention (TPA)-based LSTM is applied to the DL model to capture inherent correlations among random numbers in this paper.…”

Section: Experimental Schemementioning

confidence: 99%

Deep Learning-Based Security Verification for a Random Number Generator Using White Chaos

Zhang

Sang

et al. 2020

Entropy

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In this paper, a deep learning (DL)-based predictive analysis is proposed to analyze the security of a non-deterministic random number generator (NRNG) using white chaos. In particular, the temporal pattern attention (TPA)-based DL model is employed to learn and analyze the data from both stages of the NRNG: the output data of a chaotic external-cavity semiconductor laser (ECL) and the final output data of the NRNG. For the ECL stage, the results show that the model successfully detects inherent correlations caused by the time-delay signature. After optical heterodyning of two chaotic ECLs and minimal post-processing are introduced, the model detects no patterns among corresponding data. It demonstrates that the NRNG has the strong resistance against the predictive model. Prior to these works, the powerful predictive capability of the model is investigated and demonstrated by applying it to a random number generator (RNG) using linear congruential algorithm. Our research shows that the DL-based predictive model is expected to provide an efficient supplement for evaluating the security and quality of RNGs.

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“…Zhu et al proposed a multivariate method for ultra-short-term wind power forecasting based on long short-term memory (LSTM) to forecast the ultra-short-term wind power [19]. As the algorithm has its distinct advantages and disadvantages, some works about utilizing hybrid deep learning algorithms were also discussed in [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

Huang

Liu

et al. 2021

Energies

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More accurate wind power prediction (WPP) is of great significance for the operation of electrical power systems, as offshore wind power penetration increases continuously. As the offshore wind turbines (OWT) are a key system in converting offshore wind power into electrical power, maintaining their condition plays a pivotal role in WPP. However, it is seldom considered in traditional WPP. This paper proposes an ultra-short term offshore WPP methodology based on the condition assessment (CA) of OWTs. Firstly, a modified fuzzy comprehensive evaluation (MFCE) based CA of the OWT is presented with a new defined deterioration of indicators calculated by the relative errors. Long short-term memory (LSTM) neural network is introduced to deal with the complicated interactions between the various monitoring data of an OWT and the dynamic marine environment. Then, with the classifications of the health conditions of the OWT, the historical operation data is classified accordingly. An OWT-condition based WPP with a backpropagation (BP) neural network is developed to deal with the non-linear mapping relations between the numerical weather prediction (NWP) information, health conditions of OWT, and the output power. The results of the case study show the influences of the OWT health conditions to its output power and verifies the effectiveness and higher accuracy of the proposed method.

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Wind power forecasting using attention-based gated recurrent unit network

Cited by 236 publications

References 39 publications

Deep Learning for Time Series Forecasting: A Survey

Deep Learning for Time Series Forecasting: A Survey

Deep Learning-Based Security Verification for a Random Number Generator Using White Chaos

Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

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