Particle Swarm Optimization-Extreme Learning Machine Model Combined With the Gradient Boost Algorithm for Short-Term Wind Power Prediction

Accurate wind power assesses are fundamental to maximizing the use of wind power and defend a safe and reliable electricity grid. Gradient boosting algorithm with a limit learning machine optimized using particle swarm optimization (PSO-ELM), taking into account the inherent unpredictability and variability of wind power as well as the limitations of existing forecasting models. A Gradient Boost technique is then used to generate a large number of PSO-ELM weak predictors. Input weights and thresholds of each weak predictor, which each includes a unique hidden layer node. The results from each weak predictor are combined and weighed to get the final forecast result using a robust wind power forecast model. We use measured data from Turkish wind turbines to verify the efficacy of our suggested strategy. The findings demonstrate that the Gradient-PSO-ELM wind power prediction model has improved generalizability and accuracy than the Random forest.