A multi-component hybrid system based on predictability recognition and modified multi-objective optimization for ultra-short-term onshore wind speed forecasting

The wind is a natural source of energy and wind energy occupies an important share in the global energy structure. Compared with offshore wind energy, the economy and availability of onshore wind energy make it dominate the wind energy industry at this stage. However, the instability and inconsistency of the ultra-short-term onshore wind speed will cause inefficiency with the turbines. Therefore, accurate onshore wind speed forecasts can help onshore wind farms enhance the efficiency of wind turbines and improve the accuracy of energy assessment. Most existing forecasting methods usually suffer from insufficient precision and high complexity. To improve these deficiencies, a multi-component hybrid onshore wind speed forecasting system based on predictability recognition framework, k point modified multi-objective golden eagle optimizer, and weight hybrid kernel extreme learning machine is proposed in this study. According to the experiments of four onshore wind speed series collected from an onshore wind farm located in the US, the proposed system shows superior performances on one-step ahead and multi-step ahead forecast and it outperforms some typical methods. And the statistical significance of superior forecasting performance is fully demonstrated. Overall, the proposed forecasting system can offer a more reliable forecast for onshore wind speed. (C)& nbsp;2022 Elsevier Ltd. All rights reserved.

Automated summary

This study proposes a multi-component hybrid onshore wind speed forecasting system based on predictability recognition framework, k point modified multi-objective golden eagle optimizer, and weight hybrid kernel extreme learning machine. The system shows superior performances on one-step ahead and multi-step ahead forecast and outperforms some typical methods. It can offer a more reliable forecast for onshore wind speed.