Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer. (July 2020)

• Record Type: Journal Article
• Authors: Lu, Hongfang; Ma, Xin; Huang, Kun; Azimi, Mohammadamin
• Is Part Of: Renewable & sustainable energy reviews. Volume 127(2020)
• Publication Date: 2020-07
• Subjects: Wind power -- Multi-objective grey wolf optimizer -- Offshore wind farm -- Machine learning -- Prediction -- Kernel-based model; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2020.109856
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: The development of the wind power market has led various countries to begin shifting the construction of wind farms from land to offshore. Accurately predicting the short-term wind power of an offshore wind farm is significant for preventive control and scheduling. This paper proposes a novel two-stage hybrid model to predict short-term wind power. First, complete ensemble empirical mode decomposition with adaptive noise is used to preprocess the raw data and make it smoother. Second, an improved grey-box model is utilized to predict the wind power, where a multi-objective grey wolf optimizer is used to optimize the kernel-based nonlinear extension of the Arps decline model to ensure both prediction stability and accuracy. The historical wind power data for offshore wind farms in Belgium are taken as a case study, and the results indicate that the proposed model has higher prediction accuracy and stability than those of six benchmark models. Three critical issues related to this work are also considered. The following conclusions are reached: (1) Utilizing a denoising method and multi-objective optimizer effectively improves the prediction accuracy and stability of the original model. Specifically, the multi-objective optimizer improves the prediction performance; (2) The proposed model performs best at predicting wind power in autumn; (3) Because of the great variation in dimensions for the wind power time series, the mean absolute percentage error and root mean squared percentage error are not suitable as error evaluation indicators. Highlights: Two-stage hybrid model for predicting wind power of offshore wind farms. Multi-objective algorithm optimizes the traditional kernel-based model. Decomposition algorithm preprocesses raw data. Rationality of the error indicators for the prediction results discussed.