Cited by MIG-EWPFS: An ensemble probabilistic wind speed forecasting system integrating multi-dimensional feature extraction, hybrid quantile regression, and Knee improved multi-objective optimization

Wind power data cleaning using RANSAC-based polynomial and linear regression with adaptive threshold DOI

Haipeng Yang,

Jie Tang,

Wu Shao

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 11, 2025

As the global demand for clean energy continues to rise, wind power has become one of most important renewable sources. However, data often contains a high proportion dense anomalies, which not only significantly affect accuracy forecasting models but may also mislead grid scheduling decisions, thereby jeopardizing security. To address this issue, paper proposes an adaptive threshold robust regression model (RPR model) based on combination Random Sample Consensus (RANSAC) algorithm and polynomial linear cleaning. The successfully captures nonlinear relationship between speed by extending features power, enabling handle nonlinearity. By combining RANSAC regression, is constructed tackle anomalous enhance During cleaning process, first fits raw randomly selecting minimal sample set, then dynamically adjusts decision thresholds median residuals absolute deviation (MAD), ensuring effective identification data. model's robustness allows it maintain efficient performance even with data, addressing limitations existing methods when handling densely distributed anomalies. effectiveness innovation proposed method were validated applying real from farm operated Longyuan Power. Compared other commonly used methods, such as Bidirectional Change Point Grouping Quartile Statistical Model, Principal Contour Image Processing DBSCAN Clustering Support Vector Machine (SVM) experimental results showed that delivered best in improving quality. Specifically, reduced average error (MAE) 72.1%, higher than reductions observed (ranging 37.3 52.7%). Moreover, effectively prediction Convolutional Neural Network (CNN) + Gated Recurrent Unit (GRU) model, accuracy. study innovative significant application potential. It provides new approach cleaning, applicable conventional scenarios low proportions complex datasets

Language: Английский

Citations

Robust deep learning model with attention framework for spatiotemporal forecasting of solar and wind energy production DOI

Md. Shadman Abid, Razzaqul Ahshan, Mohammed Al‐Abri

et al.

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: unknown, P. 100919 - 100919

Published: Feb. 1, 2025

Language: Английский

Citations

CRAformer: a cross-residual attention transformer for solar irradiation multistep forecasting DOI

Zongbin Zhang,

Xiaoqiao Huang, Chengli Li

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135214 - 135214

Published: Feb. 1, 2025

Language: Английский

Citations

Improved bidirectional long short-term memory network-based short-term forecasting of photovoltaic power for different seasonal types and weather factors DOI

Ruixian Wang,

Rui Ma,

Linjun Zeng

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110219 - 110219

Published: March 1, 2025

Language: Английский

Citations

Applying artificial intelligence for forecasting behavior in a liquefied hydrogen unit DOI

Dongmei Jing,

Azher M. Abed, Pinank Patel

et al.

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 114, P. 31 - 51

Published: March 1, 2025

Language: Английский

Citations

Assessing the Potential Impact of Aerosol Scenarios for Rooftop PV Regional Deployment DOI

Bingchun Liu, S. P. Zhao, Shize Zheng

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122869 - 122869

Published: March 1, 2025

Language: Английский

Citations

Long-term policy guidance for sustainable energy transition in Nigeria: A deep learning-based peak load forecasting with econo-environmental scenario analysis DOI

Israel A. Bayode, Abdulrahman H. Ba-Alawi, Hai-Tra Nguyen

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135707 - 135707

Published: March 1, 2025

Language: Английский

Citations

A Microseismic Phase Picking and Polarity Determination Model Based on the Earthquake Transformer DOI

Ling Peng, Lei Li,

Xiaobao Zeng

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3424 - 3424

Published: March 21, 2025

Phase arrival times and polarities provide essential kinematic constraints for dynamic insights into seismic sources, respectively. This information serves as fundamental data in seismological study. For microseismic events with smaller magnitudes, reliable phase picking polarity determination are even more challenging but play a crucial role source location focal mechanism inversion. study innovatively proposes deep learning model suitable simultaneous continuous waveforms. Building upon the Earthquake Transformer (EQT) model, we implemented structural improvements through four distinct decoders specifically designed three tasks of P-wave picking, S-wave first-motion named EQT-Plus (EQTP). Notably, task was decomposed two independent to enhance characteristics. Through training on northern California dataset testing (Md < 3) Geysers region, results demonstrate that EQTP achieves superior performance both compared PhaseNet+ model. It not only provides accurate also shows higher consistency manual determination. We further validated good generalization ability DiTing from China. advances adaptation seismology reliably delivers refined inversion, offering an alternative advanced tool community.

Language: Английский

Citations

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

Huawei Mei, Qingyuan Zhu,

Cao Wangbin

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 123142 - 123142

Published: April 1, 2025

Language: Английский

Citations

MIG-EWPFS: An ensemble probabilistic wind speed forecasting system integrating multi-dimensional feature extraction, hybrid quantile regression, and Knee improved multi-objective optimization DOI

Qianyi Xing, Xiaojia Huang, Kang Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: 324, P. 136060 - 136060

Published: April 23, 2025

Language: Английский

Citations