A Novel Approach for State of Health Estimation and Remaining Useful Life Prediction of Supercapacitors Using an Improved Honey Badger Algorithm Assisted Hybrid Neural Network

Protection and Control of Modern Power Systems, Год журнала: 2024, Номер 9(6), С. 1 - 18

Опубликована: Ноя. 1, 2024

Supercapacitors (SCs) are widely recognized as excellent clean energy storage devices. Accurate state of health (SOH) estimation and remaining useful life (RUL) prediction essential for ensuring their safe reliable operation. This paper introduces a novel method SOH RUL prediction, based on hybrid neural network optimized by an improved honey badger algorithm (HBA). The combines the advantages convolutional (CNN) bidirectional long-short-term memory (BiLSTM) network. HBA optimizes hyperparameters CNN automatically extracts deep features from time series data reduces dimensionality, which then used input BiLSTM. Additionally, recurrent dropout is introduced in layer to reduce overfitting facilitate learning process. approach not only improves accuracy estimates forecasts but also significantly processing time. SCs under different working conditions validate proposed method. results show that model effectively features, enriches local details, enhances global perception capabilities. outperforms single models, reducing root mean square error below 1%, offers higher robustness compared other methods.

Язык: Английский

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A multi-energy loads forecasting model based on dual attention mechanism and multi-scale hierarchical residual network with gated recurrent unit

Energy, Год журнала: 2025, Номер 320, С. 134975 - 134975

Опубликована: Фев. 18, 2025

Язык: Английский

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Flexible energy storage estimation for electric buses: A hybrid data-driven and physical model-driven approach

Journal of Energy Storage, Год журнала: 2025, Номер 119, С. 116230 - 116230

Опубликована: Март 25, 2025

Язык: Английский

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Advancing Short-Term Load Forecasting with decomposed Fourier ARIMA: A Case Study on the Greek Energy Market

Energy, Год журнала: 2025, Номер unknown, С. 135854 - 135854

Опубликована: Март 1, 2025

Язык: Английский

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Online decoupling feature framework for optimal probabilistic load forecasting in concept drift environments

Applied Energy, Год журнала: 2025, Номер 392, С. 125952 - 125952

Опубликована: Апрель 25, 2025

Язык: Английский

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A probabilistic load forecasting method for multi-energy loads based on inflection point optimization and integrated feature screening

Energy, Год журнала: 2025, Номер unknown, С. 136391 - 136391

Опубликована: Май 1, 2025

Язык: Английский

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A systematic review of modeling method of multi-energy coupling and conversion for urban buildings

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115886 - 115886

Опубликована: Май 1, 2025

Язык: Английский

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A double deep reinforcement learning-based adaptive framework for decision-optimal wind power interval prediction

Energy, Год журнала: 2025, Номер unknown, С. 136661 - 136661

Опубликована: Май 1, 2025

Язык: Английский

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CEEMDAN-SE-HDBSCAN-VMD-TCN-BiGRU: A two-stage decomposition-based parallel model for multi-altitude ultra-short-term wind speed forecasting

Energy, Год журнала: 2025, Номер unknown, С. 136660 - 136660

Опубликована: Июнь 1, 2025

Язык: Английский

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Sequence signal prediction and reconstruction for multi-energy load forecasting in integrated energy systems: A bi-level multi-task learning method

Energy, Год журнала: 2024, Номер unknown, С. 133960 - 133960

Опубликована: Ноя. 1, 2024

Язык: Английский

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