Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis

Expert Systems with Applications, Год журнала: 2023, Номер 237, С. 121464 - 121464

Опубликована: Сен. 7, 2023

Язык: Английский

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Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting

Energy, Год журнала: 2023, Номер 269, С. 126844 - 126844

Опубликована: Янв. 30, 2023

Язык: Английский

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A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM

Energy, Год журнала: 2024, Номер 294, С. 130726 - 130726

Опубликована: Фев. 16, 2024

Язык: Английский

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An innovative interpretable combined learning model for wind speed forecasting

Applied Energy, Год журнала: 2024, Номер 358, С. 122553 - 122553

Опубликована: Янв. 5, 2024

Язык: Английский

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An overview of deterministic and probabilistic forecasting methods of wind energy

iScience, Год журнала: 2022, Номер 26(1), С. 105804 - 105804

Опубликована: Дек. 14, 2022

In recent years, a variety of wind forecasting models have been developed, prompting necessity to review the abundant methods gain insights state-of-the-art development status. However, existing literature reviews only focus on subclass methods, such as multi-objective optimization and machine learning while lacking full particulars field. Furthermore, classification is unclear incomplete, especially considering rapid this Therefore, article aims provide systematic deterministic probabilistic from perspectives data source, model evaluation framework, technical background, theoretical basis, performance. It expected that work will junior researchers with broad detailed information for their future more accurate practical models.

Язык: Английский

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Short-term wind power prediction method based on CEEMDAN-GWO-Bi-LSTM

Energy Reports, Год журнала: 2024, Номер 11, С. 1487 - 1502

Опубликована: Янв. 18, 2024

In order to improve the short-term prediction accuracy of wind power and provide basis for grid dispatching, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) -grey wolf optimization (GWO) -bidirectional long memory network (Bi-LSTM) model is proposed predict output farms. Firstly, original data preprocessed, then decomposed into components that are easy extract features by using CEEMDAN. The Bi-LSTM established each component, grey algorithm used optimize parameters model. optimized hyperparameters brought results component. Finally, component superimposed reconstructed obtain final power. simulation analysis farm in Gansu Province shows CEEMDAN-GWO-Bi-LSTM has better prediction.

Язык: Английский

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A fuzzy time series forecasting model with both accuracy and interpretability is used to forecast wind power

Applied Energy, Год журнала: 2023, Номер 353, С. 122015 - 122015

Опубликована: Окт. 4, 2023

Язык: Английский

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Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach

Pattern Recognition, Год журнала: 2024, Номер 155, С. 110637 - 110637

Опубликована: Май 31, 2024

Язык: Английский

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Robformer: A robust decomposition transformer for long-term time series forecasting

Pattern Recognition, Год журнала: 2024, Номер 153, С. 110552 - 110552

Опубликована: Май 3, 2024

Язык: Английский

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Quantile deep learning model and multi-objective opposition elite marine predator optimization algorithm for wind speed prediction

Applied Mathematical Modelling, Год журнала: 2022, Номер 115, С. 56 - 79

Опубликована: Ноя. 5, 2022

Язык: Английский

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