Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed

Elsaraiti, Meftah; Merabet, Adel

doi:10.3390/app11052387

Cited by 42 publications

(15 citation statements)

References 12 publications

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“…A recurrent neural network (RNN) is mainly used to learn ordered data or time-series data such as natural language processing and speech recognition [50][51][52][53][54][55][56][57][58][59]. However, RNN has the vanishing gradient problem that significantly reduces the learning ability when the distance between the previous output and the point where it uses the information from that output is far away [60,61].…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

Jeong

Cho

et al. 2021

Applied Sciences

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Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

Jeong

Cho

et al. 2021

Applied Sciences

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“…Wind speed data consist of sequential data, where the prediction model requires to consider information relevant to the previous steps in the sequence (Elsaraiti and Merabet, 2021). Recurrent neural networks (RNN) outperform adaptive neural networks (Bollt, 2021) in learning the long-term dependency for time series forecasting (Kumar et al, 2020).…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…Recurrent neural networks (RNN) outperform adaptive neural networks (Bollt, 2021) in learning the long-term dependency for time series forecasting (Kumar et al, 2020). The RNN structure consists of hidden layers distributed across time (Elsaraiti and Merabet, 2021) that enables the achievement of information from the previous state of reading historical data (Duan et al, 2021).…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

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Weather forecasting based on data-driven and physics-informed reservoir computing models

Mammedov

Olugu

Farah

2021

Environ Sci Pollut Res

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In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behaviour of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the out-performance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

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“…However, meteorological data have been used in the abovementioned works, which are not suitable for the regional ultra-short-term wind power prediction. Machine learning methods continue to learn the mapping relationship between input data and output data through training a large number of data samples (Elsaraiti and Merabet, 2021), providing improvement for power prediction only based on historical data. In order to further explore the variation law of wind power data with time and improve the prediction accuracy of training models, many scholars choose wavelet decomposition, Empirical Mode Decomposition (EMD) (Huang et al, 1998), Ensemble Empirical Mode Decomposition (EEMD) (Wu and Huang, 2011), and other methods to decompose the historical data into several regular subsequences (Safari et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Regional Ultra-Short-Term Wind Power Combination Prediction Method Based on Fluctuant/Smooth Components Division

Yan

He³

et al. 2022

Front. Energy Res.

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When multiple scattered wind farms are connected to the power grid, the meteorological and geographic information data used for power prediction of a single wind farm are not suitable for the regional wind power prediction of the dispatching department. Therefore, based on the regional wind power historical data, this study proposes a combined prediction method according to data decomposition. Firstly, the original sequence processed by the extension methods is decomposed into several regular components by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). All the components are classified into two categories: fluctuant components and smooth components. Then, according to the characteristics of different data, the long short-term memory (LSTM) network and autoregressive integrated moving average (ARIMA) model are used to model the fluctuant components and the smooth components, respectively, and obtain the predicted values of each component. Finally, the predicted data of all components are accumulated, which is the final predicted result of the regional ultra-short-term wind power. The feasibility and accuracy of this method are verified by the comparative analysis.

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Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed

Cited by 42 publications

References 12 publications

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

Weather forecasting based on data-driven and physics-informed reservoir computing models

Regional Ultra-Short-Term Wind Power Combination Prediction Method Based on Fluctuant/Smooth Components Division

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