A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization. (15th January 2022) - Guo, Honggang; Wang, Jianzhou; Li, Zhiwu; Jin, Yu
Abstract: Wind power forecasting is critical to the safe running of the power grid. However, due to the strong intermittence and instability of wind, reliable forecast of wind power remains a significant difficulty. In this study, a novel multivariable machine learning hybrid prediction system that incorporates data preprocessing, prediction, and multi-objective system optimization is designed to quantify the certainty and uncertainty of wind power. To increase the quality of data input, the data preparation module performs outlier tests based on the correlation between wind power and wind speed, as well as feature extraction, on the original data. In the prediction process, this paper offers an incremental kernel extreme learning machine (IK-elm), the parameters of which are set synchronously by an enhanced multi-objective optimization technique (MOCEHHO) developed in this paper. It overcomes the restrictions of duplicated hidden layer nodes and low learning efficiency caused by classic ELM and successfully maximizes the model's prediction capabilities. The simulation results on four datasets from Turkish wind farms show that the hybrid forecasting system outperforms the benchmark and may be utilized as a useful tool for wind power forecasting. Highlights: A novel multivariate hybrid wind power forecasting system is proposed with good performance. The outliers are identified by the correlation curve of wind power and wind speed. A innovation improved multi-objective optimization algorithm is proposed. IK-elm is used to improve the generalization ability and learning efficiency.