Short-term wind power forecasting with lag-based feature engineering: a turbine-level machine learning comparison

SCADA-based feature analysis and Bayesian-optimized machine learning for short-term turbine-level wind power forecasting

Most wind power forecasting studies rely primarily on historical turbine output time series, while environmental and operational parameters remain insufficiently explored. This paper investigates short-term turbine-level wind power forecasting using SCADA data collected from a wind farm in Vietnam, incorporating both e...

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...

Hybrid transformer model for wind power generation forecasting

A Comparative Study of Ensemble Tree-Based Models for Short-Term Electricity Load Forecasting

Short-term load forecasting (STLF) is an essential task for reliable power system operation, economic dispatch, reserve scheduling, and grid planning. This study aims to provide an operationally realistic and interpretable comparison of five ensemble tree-based machine learning (ML) models for national electricity dema...

Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts

A baseline Linear Regression model is developed for performance comparison with a more complex Artificial Neural Network model to predict the power output of a wind turbine, using weather conditions only to enhance applicability.

Enhanced hybrid residual learning framework for robust wind turbine power prediction using machine learning

This research work captures the variation in wind resource variability by implementing advanced machine learning models for short-term power forecasting using SCADA data using a fresh framework to focus on parameters such as aerodynamic behavior, temporal patterns, and overall regime.