Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC

Energies, Год журнала: 2025, Номер 18(7), С. 1646 - 1646

Опубликована: Март 25, 2025

Accurate forecasting is crucial for enhancing the flexibility and controllability of power grids. Traditional methods mainly focus on modeling based a single data source, which leads to an inability fully capture underlying relationships in wind data. In addition, current models often lack dynamic adaptability characteristics, resulting lower prediction accuracy reliability under different time periods or weather conditions. To address aforementioned issues, ultra-short-term hybrid probabilistic model MultiFusion, ChronoNet, adaptive Monte Carlo (AMC) proposed this paper. By combining multi-source fusion multiple-gated structure, nonlinear characteristics uncertainties various input conditions are effectively captured by model. Additionally, AMC method applied paper provide comprehensive, accurate, flexible predictions. Ultimately, experiments conducted multiple datasets, results show that not only improves deterministic but also enhances intervals.

Язык: Английский

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Sustainable AI-driven wind energy forecasting: advancing zero-carbon cities and environmental computation

Artificial Intelligence Review, Год журнала: 2025, Номер 58(6)

Опубликована: Март 29, 2025

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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Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 15, 2025

Язык: Английский

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A robust energy flow predictor based on CNN-LSTM for prosumer-oriented microgrids considering changes in biogas generation

Energy, Год журнала: 2025, Номер unknown, С. 136050 - 136050

Опубликована: Апрель 1, 2025

Язык: Английский

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A Novel Hybrid Method for Multi-Step Short-Term 70 m Wind Speed Prediction Based on Modal Reconstruction and STL-VMD-BiLSTM

Atmosphere, Год журнала: 2024, Номер 15(8), С. 1014 - 1014

Опубликована: Авг. 21, 2024

In the context of achieving goals carbon peaking and neutrality, development clean resources has become an essential strategic support for low-carbon energy transition. This paper presents a method modal decomposition reconstruction time series to enhance prediction accuracy performance regarding 70 m wind speed. The experimental results indicate that STL-VMD-BiLSTM hybrid algorithm proposed in this outperforms STL-BiLSTM VMD-BiLSTM models forecasting accuracy, particularly extracting nonlinearity characteristics effectively capturing speed extremes. Compared with other machine learning algorithms, including STL-VMD-LGBM, STL-VMD-SVR STL-VMD-RF models, model demonstrates superior performance. average evaluation criteria, RMSE, MAE R2, model, from t + 15 120 show improvements 0.582–0.753 m/s, 0.437–0.573 m/s 0.915–0.951, respectively.

Язык: Английский

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Motion interval prediction of a sea satellite launch platform based on VMD-QR-GRU

Ocean Engineering, Год журнала: 2024, Номер 312, С. 119005 - 119005

Опубликована: Авг. 30, 2024

Язык: Английский

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A novel approach for multivariate time series interval prediction of water quality at wastewater treatment plants

Water Science & Technology, Год журнала: 2024, Номер 90(10), С. 2813 - 2841

Опубликована: Ноя. 12, 2024

ABSTRACT This study proposes a novel approach for predicting variations in water quality at wastewater treatment plants (WWTPs), which is crucial optimizing process management and pollution control. The model combines convolutional bi-directional gated recursive units (CBGRUs) with adaptive bandwidth kernel function density estimation (ABKDE) to address the challenge of multivariate time series interval prediction WWTP quality. Initially, wavelet transform (WT) was employed smooth data, reducing noise fluctuations. Linear correlation coefficient (CC) non-linear mutual information (MI) techniques were then utilized select input variables. CBGRU applied capture temporal correlations series, integrating Multiple Heads Attention (MHA) mechanism enhance model's ability comprehend complex relationships within data. ABKDE employed, supplemented by bootstrap establish upper lower bounds intervals. Ablation experiments comparative analyses benchmark models confirmed superior performance point prediction, analysis forecast period, fluctuation detection Also, this verifies broad applicability robustness anomalous contributes significantly improved effluent efficiency control WWTPs.

Язык: Английский

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Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead

Applied Sciences, Год журнала: 2024, Номер 14(24), С. 11918 - 11918

Опубликована: Дек. 19, 2024

Accurate wind speed and power forecasting are key to optimizing renewable station management, which is essential for smart zero-energy cities. This paper presents a novel integrated speed–power system (WSPFS) that operates across various time horizons, demonstrated through case study in high-wind area within the Middle East. The WSPFS leverages 12 AI algorithms both individual ensemble models forecast (WSF) (WPF) at intervals of 10 min 36 h. A multi-horizon prediction approach proposed, using WSF model outputs as inputs WPF modeling. Predictive accuracy was evaluated mean absolute percentage error (MAPE) square (MSE). Additionally, advances energy deep decarbonization (SWEDD) framework by calculating carbon city index (CCI) define carbon-city transformation curve (CCTC). Findings from this have broad implications, enabling urban projects mega-developments like NEOM Suez Canal advancing global trading supply management.

Язык: Английский

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Data-driven fault detection framework for offshore wind-hydrogen systems

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 70, С. 325 - 340

Опубликована: Май 17, 2024

Язык: Английский

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