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Conference Aug 2026

Short-Term Wind Power Forecasting Using Transformer-XGBoost Hybrid Model

Accurate wind forecasting is critical to ensure stable and efficient integration of renewable energy resources in modern power systems. However, the inherent variability and non-stationarity of wind pose a significant forecasting problem for modern power system operators to ensure power system stability. A new hybrid f...

Heshan Senapriya, Sakun Rasilka, D. P. Wadduwage · 0 citations
Open access Sep 2026

Initial-State-Aware Multi-Scale Transformer for Short-Term Wind Power Forecasting

Accurate short-term wind power forecasting is essential for the secure and economic operation of power systems with high renewable energy penetration. However, forecasting performance is still affected by meteorological forecast uncertainty and the complex multi-scale fluctuation characteristics of wind power generatio...

Chao-Ying Yang, Jun Zhao, Peng Han et al. · 0 citations
Open access Sep 2026

Empowering Wind Energy Output Optimization: Comparative Assessment of Hybrid Artificial Intelligence Models Towards Wind Speed Forecasting Accuracy

This study proposes a hybrid machine learning approach that combines the strengths of random forest regression, artificial neural network, and support vector regression as a meta-model for wind speed forecasting, and finds that temperature, relative humidity, and cyclical time-encoded features are the most important in...

Latif Bukari Rashid, S. Rehman, Mohamad E. Zayed et al. · 1 citation
Open access Sep 2026

Does Machine Learning Improve Wind Power Forecasting? An Experimental Investigation

A systematic experimental investigation that covers two complementary stages, i.e., wind speed correction and wind power forecasting, provides actionable task-specific guidance for model selection in operational wind power forecasting systems.

Zhi-Min Li, Yu Chen, Tingzhao Yu et al. · 0 citations
Open access Aug 2026

Transformer-based short-term forecasting of renewable power and grid load using high-resolution weather data with explainable AI

Accurate forecasting of electricity load, solar power, and wind power is critical to the reliability of modern power systems, particularly in regions such as California with high penetration of variable power sources. California’s power system is managed by the California Independent System Operator (CAISO), which oper...

Dajiang Hu, Na Liu · 0 citations
Open access Aug 2026

An MT-Transformer Framework for Coordinated Wind-Solar-Load Forecasting with Net-Load-Based Coal-Power Regulation Demand Identification

Findings confirm that the proposed MT-Transformer framework improves coordinated forecasting performance and provides quantitative evidence for coal-power peak regulation, reserve capacity allocation, and ancillary service demand identification.

Meng Huang, Lei Wang, Teng Luo et al. · 0 citations

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