Search Results - "Wind forecasting"

A Hybrid Wind Speed Forecasting Framework Based on Downscaled Multi-Model Forecasts and Machine Learning for Day-Ahead Wind Power Applications.

Authors: Oh, Donggun Lee, Minkyu Oh, Myeongchan et al.

Source: Energies (19961073). Jun2026, Vol. 19 Issue 12, p2928. 26p.

Subject Terms: *Wind forecasting, *Machine learning, *Numerical weather forecasting, *Downscaling (Climatology), *Renewable energy sources, *Wind power, *Forecasting

Geographic Terms: Jeju Island (Korea)

Bridging Equation-Based and Data-Driven Dynamics for Reliable Wind Speed Prediction in Energy Systems.

Authors: Yu, Hangyi Gao, Sheng Zhao, Hanqing et al.

Source: Energies (19961073). Jun2026, Vol. 19 Issue 12, p2847. 24p.

Subject Terms: *Wind forecasting, *Partial differential equations, *Energy infrastructure, *Dynamical systems, *Deep learning, *Atmospheric circulation, *Spatiotemporal processes

A Comparative Study of Day-Ahead Wind Power Forecasting Models for a Single Wind Farm Under Strict Chronological Splitting and Unified Hyperparameter Tuning Conditions.

Authors: Liu, Jiacheng Lu, Yihang Zou, Guoping

Source: Energies (19961073). Jun2026, Vol. 19 Issue 12, p2784. 31p.

Subject Terms: *Wind forecasting, *Wind power plants, *Machine learning, *Benchmarking (Management), *Forecasting

Short-Term Wind Power Forecasting Based on Dual-Optimized VMD-CNN-BiLSTM.

Authors: Sun, Xiaohan Han, Bing Song, Yuting et al.

Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2317. 24p.

Subject Terms: *Wind forecasting, *Optimization algorithms, *Deep learning, *Metaheuristic algorithms, *Long short-term memory, *Convolutional neural networks

STFNet: A Specialized Time-Frequency Domain Feature Extraction Neural Network for Long-Term Wind Power Forecasting.

Authors: Ding, Tingxiao Hu, Xiaochun Chen, Yan et al.

Source: Energies (19961073). May2026, Vol. 19 Issue 9, p2080. 22p.

Subject Terms: *Wind forecasting, *Time-frequency analysis, *Convolutional neural networks, *Artificial neural networks, *Renewable energy source management, *Signal processing, *Machine learning, *Renewable energy sources

A Hybrid Linear–Gaussian Process Framework with Adaptive Covariance Selection for Spatio-Temporal Wind Speed Forecasting.

Authors: Tshisikhawe, Thinawanga Hangwani Sigauke, Caston Darikwa, Timotheous Brian et al.

Source: Forecasting. Jun2026, Vol. 8 Issue 3, p36. 37p.

Transforming Wind Nowcasting: Innovative Strategies for Next-Frame Prediction Using Conv-LSTM-3D Model.

Authors: B. Upadhyay, Abhay R. Shah, Saurin A. Thakkar, Rajesh

Source: Iraqi Journal for Electrical & Electronic Engineering. Jun2026, Vol. 22 Issue 1, p36-45. 10p.

Subjects: Wind forecasting, Deep learning, Machine learning, Forecasting, Spatiotemporal processes, Weather forecasting

Wind speed prediction and energy estimation using the SARIMA method in Banyumas Regency.

Authors: Yuniarto, Abdul Hakim Prima Nawangnugraeni, Devi Astri Admaja, Rafif Aldo et al.

Source: International Journal of Electrical & Computer Engineering (2088-8708). Jun2026, Vol. 16 Issue 3, p1425-1433. 9p.

Subjects: Wind forecasting, Wind power, Machine learning, Renewable energy sources, Electric power consumption, Box-Jenkins forecasting

A Comprehensive Case Study of Wind Energy Production Forecasting in Türkiye Using Enhanced Attention BiLSTM.

Authors: Çeçen, Mehmet Rivera, Marco Yavuz, Mehmet

Source: Wind Energy. May2026, Vol. 29 Issue 5, p1-17. 17p.

Subjects: Wind forecasting, Long short-term memory, Meteorological databases, Renewable energy sources, Energy consumption, Machine learning

Geographic Terms: Turkey

Short-to-Medium Term Ocean Wind Speed Prediction via Sparse Grid Dynamic Spatial Modeling and DAI-LSTM-AT Hybrid Framework.

Authors: Guo, Qiaoying Chen, Rengyu Dong, Dibo et al.

Source: Remote Sensing. May2026, Vol. 18 Issue 9, p1405. 27p.

Subjects: Wind forecasting, Deep learning, Spatiotemporal processes, Wind power, Maritime safety

An Adaptive Multi-Scale Heterogeneous Ensemble Framework for Interpretable Wind Power Forecasting in Sustainable Grids.

Authors: Gao, Jiaoyang Zhang, Hui Sun, Zhongmiao et al.

Source: Symmetry (20738994). Jun2026, Vol. 18 Issue 6, p921. 36p.

Observational and Forecasting Aspects of Super Typhoon Ragasa in Hong Kong.

Authors: Lai, S. K. He, Y. H. Ho, C. K. et al.

Source: Tropical Cyclone Research & Review. Jun2026, Vol. 15 Issue 2, p255-273. 19p.

A Short-Term Wind Power Prediction Method Based on Multi-Model Fusion with an Improved Gray Wolf Optimization Algorithm.

Authors: Yu, Zaijiang Jiang, He Zhao, Yan

Source: Algorithms. May2026, Vol. 19 Issue 5, p339. 25p.

Artificial Intelligence-Driven Sensing Framework with Multimodal Sensor Importance Learning for Smart Energy Systems.

Authors: Zhang, Shujin Liu, Zhuochen Sun, Kai et al.

Source: Sensors (14248220). May2026, Vol. 26 Issue 9, p2791. 23p.

A hybrid VMD-PermEn-DBSCAN-ICEEMDAN deep learning framework for ultra-short-term wind speed prediction.

Authors: Alrashidi, Musaed¹ (AUTHOR) malrashidi@qu.edu.sa

Source: AIMS Mathematics. 2026, Vol. 11 Issue 5, p1-32. 32p.

STGCformer: Spatio-Temporal Graph Convolutional Transformer for Short-Term Wind Power Forecasting.

Authors: Tian, Chenyu Xia, Min Yuan, Shi et al.

Source: Energies (19961073). Mar2026, Vol. 19 Issue 5, p1214. 17p.

Subject Terms: *Wind forecasting, *Transformer models, *Time series analysis, *Forecasting, *Graph neural networks, *Electric power system reliability

A CPO-Optimized BiTCN–BiGRU–Attention Network for Short-Term Wind Power Forecasting.

Authors: Huang, Liusong Jaharadak, Adam Amril bin Ahmad, Nor Izzati et al.

Source: Energies (19961073). Feb2026, Vol. 19 Issue 4, p1034. 19p.

Subject Terms: *Wind forecasting, *Optimization algorithms, *Electric power system stability, *Deep learning, *Forecasting, *Recurrent neural networks

A Multi-Scale Offshore Wind Power Forecasting Model Based on Data Decomposition, Intelligent Optimization Algorithms, and Multi-Modal Fusion.

Authors: Liu, Kang Sun, Yuan Han, Pengyu

Source: Energies (19961073). Feb2026, Vol. 19 Issue 4, p994. 28p.

Subject Terms: *Optimization algorithms, *Wind forecasting, *Multisensor data fusion, *Feature extraction, *Forecasting

Series-Core Fusion Based Multivariate Variational Mode Decomposition for Short-Term Wind Power Prediction Using Multiple Meteorological Data.

Authors: Lu, Wentian Lu, Zhenming Liu, Wenjie et al.

Source: Forecasting. Feb2026, Vol. 8 Issue 1, p15. 23p.

APPLICATION OF LSTM NEURAL NETWORKS WITH MULTIVARIATE NUMERICAL ANALYSIS TO AVIATION WIND GUST FORECASTING.

Authors: CHEN, Chuen-Jyh¹ chuenjyh@mail.cjcu.edu.tw

Source: Aviation (1648-7788). 2026, Vol. 30 Issue 2, p143-155. 13p.

Subjects: Wind forecasting, Recurrent neural networks, Weather hazards, Weather forecasting, Forecasting, Feature selection, Multivariate analysis