Ultra-short-term wind power prediction method based on FTI-VACA-XGB model

Expert Systems with Applications, Год журнала: 2023, Номер 235, С. 121185 - 121185

Опубликована: Авг. 12, 2023

In order to predict wind power quickly and accurately reduce the negative impact of instability on grid, this study proposes an ultra-short-term prediction model based financial technical indicators parameter optimization algorithms. Firstly, historical time series data calculates indicators. Secondly, Monte Carlo method rank ant colony algorithm are used optimize parameters calculation. Finally, future is predicted XGBoost combining with power. The proposed validated several datasets from different countries compared various comparative models, leading important conclusions: (1) Fintech can effectively indicate intrinsic characteristics time-series data. (2) variational make better fit trends. (3) has high accuracy speed similar mainstream deep learning models. (4) not limited by meteorological, geographical, seasonal factors predictions relying only

Язык: Английский

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Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm

Applied Energy, Год журнала: 2023, Номер 341, С. 121108 - 121108

Опубликована: Апрель 21, 2023

Язык: Английский

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TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs

Energy, Год журнала: 2023, Номер 290, С. 130184 - 130184

Опубликована: Дек. 29, 2023

Язык: Английский

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Chaos theory meets deep learning: A new approach to time series forecasting

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124533 - 124533

Опубликована: Июнь 25, 2024

Язык: Английский

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An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model

Energy, Год журнала: 2024, Номер 293, С. 130751 - 130751

Опубликована: Фев. 19, 2024

Язык: Английский

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