COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications

Applied Energy, Год журнала: 2023, Номер 349, С. 121638 - 121638

Опубликована: Июль 27, 2023

Язык: Английский

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Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Neural Processing Letters, Год журнала: 2022, Номер 55(4), С. 4519 - 4622

Опубликована: Окт. 31, 2022

Язык: Английский

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Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks

Energy Conversion and Management, Год журнала: 2023, Номер 283, С. 116916 - 116916

Опубликована: Март 16, 2023

Язык: Английский

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Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants

Energy, Год журнала: 2023, Номер 274, С. 127350 - 127350

Опубликована: Март 30, 2023

Язык: Английский

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A review of the applications of artificial intelligence in renewable energy systems: An approach-based study

Energy Conversion and Management, Год журнала: 2024, Номер 306, С. 118207 - 118207

Опубликована: Март 16, 2024

Язык: Английский

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A novel wind power forecasting system integrating time series refining, nonlinear multi-objective optimized deep learning and linear error correction

Energy Conversion and Management, Год журнала: 2023, Номер 299, С. 117818 - 117818

Опубликована: Ноя. 16, 2023

Язык: Английский

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High and low frequency wind power prediction based on Transformer and BiGRU-Attention

Energy, Год журнала: 2023, Номер 288, С. 129753 - 129753

Опубликована: Дек. 1, 2023

Язык: Английский

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A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature

Energy, Год журнала: 2024, Номер 292, С. 130584 - 130584

Опубликована: Фев. 7, 2024

This paper presents a novel architecture, termed Fusion-Fission Optimisation (FuFi) based Convolutional Neural Network with Bi-Long Short Term Memory (FuFi-CNN-Bi-LSTM), to enhance state of charge (SoC) estimation performance. The proposed FuFi-CNN-Bi-LSTM model leverages the power both Networks (CNN) and (Bi-LSTM) while utilizing FuFi optimization effectively tune hyperparameters network. technique facilitates efficient SoC by finding optimal configuration model. A comparative analysis is conducted against Algorithm-based models, including FuFi-CNN-LSTM, FuFi-Bi-LSTM, FuFi-LSTM, FuFi-CNN. comparison involves assessing performance on tasks identifying strengths limitations models. Furthermore, undergoes rigorous testing various drive cycle tests, HPPC, HWFET, UDDS, US06, at different temperatures ranging from -20 25 degrees Celsius. model's robustness reliability are assessed under real-world operating conditions using well-established evaluation indexes, Relative Error (RE),Mean Absolute (MAE), R Square (R2), Granger Causality Test. results demonstrate that achieves across wide range higher lower ranges. signifies efficacy in accurately estimating conditions.

Язык: Английский

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Wind power forecasting method of large-scale wind turbine clusters based on DBSCAN clustering and an enhanced hunter-prey optimization algorithm

Energy Conversion and Management, Год журнала: 2024, Номер 307, С. 118341 - 118341

Опубликована: Март 28, 2024

Язык: Английский

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Read-First LSTM model: A new variant of long short term memory neural network for predicting solar radiation data

Energy Conversion and Management, Год журнала: 2024, Номер 305, С. 118267 - 118267

Опубликована: Март 7, 2024

Язык: Английский

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