A robust wind speed forecasting framework using multi-kernel correntropy optimization and adaptive residual error correction
Authors:
- Chuliang LiuCLChuliang Liu
- Zhiwei LuoZLZhiwei Luo
- Fuhong JiangFJFuhong Jiang
- Zhonghe HuangZhonghe Huang
Abstract
No abstract availableThis publication has 32 references indexed in Scilit:
- Interval multi-feature sparse transformer-CNN: A synergistic approach to precise and efficient spatio-temporal wind speed interval-value predictionApplied Soft Computing, 2025
- Enhancement of wind speed forecasting using optimized decomposition technique, entropy-based reconstruction, and evolutionary PatchTSTEnergy Conversion and Management, 2025
- A fusion approach of discrete wavelet decomposition and deep learning techniques for the enhancement of wind speed prediction accuracyTheoretical and Applied Climatology, 2025
- A location-centric transformer framework for multi-location short-term wind speed forecastingEnergy Conversion and Management, 2025
- Analysis and prediction of incoming wind speed for turbines in complex wind farm: Accounting for meteorological factors and spatiotemporal characteristics of wind farmApplied Energy, 2024
- A new decomposition-ensemble strategy fusion with correntropy optimization learning algorithms for short-term wind speed predictionApplied Energy, 2024
- Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variablesApplied Energy, 2023
- A review of wind speed and wind power forecasting with deep neural networksApplied Energy, 2021
- Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural networkEnergy, 2021
- Data processing strategies in wind energy forecasting models and applications: A comprehensive reviewApplied Energy, 2019