A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction

Energy, Год журнала: 2022, Номер 266, С. 126419 - 126419

Опубликована: Дек. 13, 2022

Язык: Английский

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Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study

Electric Power Systems Research, Год журнала: 2023, Номер 222, С. 109502 - 109502

Опубликована: Июнь 1, 2023

Язык: Английский

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Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method

Renewable Energy, Год журнала: 2023, Номер 218, С. 119357 - 119357

Опубликована: Сен. 22, 2023

Язык: Английский

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A short-term wind power forecasting method based on multivariate signal decomposition and variable selection

Applied Energy, Год журнала: 2024, Номер 360, С. 122759 - 122759

Опубликована: Фев. 6, 2024

Язык: Английский

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Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting

Applied Intelligence, Год журнала: 2024, Номер 54(4), С. 3119 - 3134

Опубликована: Фев. 1, 2024

Язык: Английский

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A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data

Expert Systems with Applications, Год журнала: 2024, Номер 247, С. 123237 - 123237

Опубликована: Янв. 19, 2024

Язык: Английский

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Wind power forecasting based on new hybrid model with TCN residual modification

Energy and AI, Год журнала: 2022, Номер 10, С. 100199 - 100199

Опубликована: Сен. 6, 2022

Wind energy has been widely utilized to alleviate the shortage of fossil resources. When wind power is integrated into grid on a large scale, grid's stability severely harmed due fluctuating and intermittent properties speed. Accurate forecasts help formulate good operational strategies for farms. A short-term forecasting method based new hybrid model proposed increase accuracy forecast. Firstly, time series are separated using complete ensemble empirical mode decomposition with adaptive noise obtain multiple components, which then predicted support vector regression machine optimized through search cross validation (GridSearchCV) algorithm. Secondly, residual modification temporal convolutional network constructed, variables high correlation selected as input features predict residuals power. Finally, prediction compared other models actual data farm demonstrate validity described method, results reveal that better performance. © 2017 Elsevier Inc. All rights reserved.

Язык: Английский

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SCADA system dataset exploration and machine learning based forecast for wind turbines

Results in Engineering, Год журнала: 2022, Номер 16, С. 100640 - 100640

Опубликована: Сен. 13, 2022

Язык: Английский

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Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction

Energy, Год журнала: 2023, Номер 286, С. 129640 - 129640

Опубликована: Ноя. 13, 2023

Язык: Английский

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Short-term wind power prediction based on modal reconstruction and CNN-BiLSTM

Energy Reports, Год журнала: 2023, Номер 9, С. 6449 - 6460

Опубликована: Июнь 16, 2023

Accurate prediction of short-term wind power plays an important role in the safe operation and economic dispatch grid. In response to current single algorithm that cannot further improve accuracy, this study proposes a combined model based on data processing, signal decomposition, deep learning. First, outliers original can affect accuracy. This detects by Z-score method fills them with cubic spline interpolation ensure integrity data. Second, for volatility power, time series is decomposed using complete ensemble empirical modal decomposition adaptive noise (CEEMDAN). The component complexity calculated sample entropy (SE), components are reconstructed according SE size Finally, traditional convolutional neural network (CNN) structure improved bi-directional long memory (BiLSTM) used extract feature links between superimpose results each obtain final value. experimental demonstrate hybrid proposed has better performance terms performance.

Язык: Английский

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