BiLSTM-InceptionV3-Transformer-fully-connected model for short-term wind power forecasting

Energy Conversion and Management, Год журнала: 2024, Номер 321, С. 119094 - 119094

Опубликована: Сен. 25, 2024

Язык: Английский

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A hybrid model based on CapSA-VMD-ResNet-GRU-attention mechanism for ultra-short-term and short-term wind speed prediction

Renewable Energy, Год журнала: 2024, Номер unknown, С. 122191 - 122191

Опубликована: Дек. 1, 2024

Язык: Английский

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Incorporating key features from structured and unstructured data for enhanced carbon trading price forecasting with interpretability analysis

Applied Energy, Год журнала: 2025, Номер 382, С. 125301 - 125301

Опубликована: Янв. 8, 2025

Язык: Английский

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Uncertainty prediction of wind speed based on improved multi-strategy hybrid models

Electronic Research Archive, Год журнала: 2025, Номер 33(1), С. 294 - 326

Опубликована: Янв. 1, 2025

<p>Accurate interval prediction of wind speed plays a vital role in ensuring the efficiency and stability power generation. Due to insufficient traditional methods for mining nonlinear features, this paper, novel method was proposed by combining improved wavelet threshold deep learning (BiTCN-BiGRU) with nutcracker optimization algorithm (NOA). First, NOA used optimize transform (WT) BiTCN-BiGRU. Second, we applied NOA-WT smooth data. Then, capture features time series, phase space reconstruction (PSR) utilized identify chaotic characteristics processed Finally, NOA-BiTCN-BiGRU model built perform prediction. Under same hyperparameters network structure settings, comparison other showed that coverage probability (PICP) mean width (PIMW) NOA-WT-BiTCN-BiGRU achieves best balance, good accuracy generalization performance. This research can provide reference guidance time-series real world.</p>

Язык: Английский

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The CEEMDAN-EWT-CNN-GRU-SVM Model: A Robust Framework for Decomposing Non-Stationary Time Series, Extracting Data features, and Predicting Solar Radiation

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104267 - 104267

Опубликована: Фев. 1, 2025

Язык: Английский

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Development of a Hybrid Deep Learning Model with Hho Algorithm for Dynamic Response Prediction of Wind-Wave Integrated Floating Energy Systems

Опубликована: Янв. 1, 2025

Язык: Английский

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A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model

Energies, Год журнала: 2025, Номер 18(5), С. 1136 - 1136

Опубликована: Фев. 25, 2025

Accurate and reliable wind speed prediction plays a significant role in ensuring the reasonable scheduling of power resources. However, sequences often exhibit complex characteristics such as instability volatility, which create substantial challenges for prediction. In order to cope with these challenges, multi-step method based on secondary decomposition (SD) techniques deep learning models is proposed this paper. First, original signal was decomposed into multiple by using two techniques, multi-scale wavelet spectrum analysis (MWPSA) variational mode (VMD). Second, model constructed combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, attention mechanism perform predicting each sequence, parameters were optimized particle swarm optimization (PSO) algorithm. Ultimately, results from all combined generate final The predictive performance evaluated real data collected farm China. Experimental show that significantly outperforms other comparison prediction, highlights its accuracy reliability.

Язык: Английский

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CMLLM: A novel cross-modal large language model for wind power forecasting

Energy Conversion and Management, Год журнала: 2025, Номер 330, С. 119673 - 119673

Опубликована: Фев. 27, 2025

Язык: Английский

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A Novel Hybrid Deep Learning Model for Day-Ahead Wind Power Interval Forecasting

Sustainability, Год журнала: 2025, Номер 17(7), С. 3239 - 3239

Опубликована: Апрель 5, 2025

Accurate interval forecasting of wind power is crucial for ensuring the safe, stable, and cost-effective operation grids. In this paper, we propose a hybrid deep learning model day-ahead forecasting. The begins by utilizing Gaussian mixture (GMM) to cluster daily data with similar distribution patterns. To optimize input features, feature selection (FS) method applied remove irrelevant data. empirical wavelet transform (EWT) then employed decompose both numerical weather prediction (NWP) into frequency components, effectively isolating high-frequency components that capture inherent randomness volatility A convolutional neural network (CNN) used extract spatial correlations meteorological while bidirectional gated recurrent unit (BiGRU) captures temporal dependencies within sequence. further enhance accuracy, multi-head self-attention mechanism (MHSAM) incorporated assign greater weight most influential elements. This leads development based on GMM-FS-EWT-CNN-BiGRU-MHSAM. proposed validated through comparison benchmark demonstrates superior performance. Furthermore, forecasts generated using NPKDE shows new achieves higher accuracy.

Язык: Английский

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A TSFLinear model for wind power prediction with feature decomposition-clustering

Renewable Energy, Год журнала: 2025, Номер unknown, С. 123142 - 123142

Опубликована: Апрель 1, 2025

Язык: Английский

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