A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting

• Published in: Applied energy Vol. 208; pp. 344 - 360
• Main Authors: Wang, Jianzhou, Du, Pei, Niu, Tong, Yang, Wendong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.12.2017
• Subjects: algorithms; Forecasting accuracy and stability; Hybrid forecasting system; managers; Multi-Objective Whale Optimization Algorithm; prediction; researchers; whales; wind speed; Wind speed forecasting; Forecasting accuracy and stability; Hybrid forecasting system; Multi-Objective Whale Optimization Algorithm; Wind speed forecasting
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2017.10.031

•We propose a new algorithm—Multi-Objective Whale Optimization Algorithm (MOWOA).•A novel hybrid system based on MOWOA is proposed for wind speed forecasting.•The new proposed model is compared to sixteen models for wind speed prediction.•The proposed hybrid system demonstrates higher prediction accuracy and reliability. In recent years, managers and researchers have paid increasing attention to accurate and stable wind speed prediction due to its significant effect on power dispatching and power grid security. However, most previous research has focused only on enhancing either accuracy or stability, with few studies addressing the two issues, simultaneously. This task is challenging due to the intermittency and complex fluctuations of wind speed. Therefore, we proposed a novel hybrid system based on a newly proposed called the MOWOA, which includes four modules: a data preprocessing module, optimization module, forecasting module, and evaluation module. An effective decomposing technique is also applied to eliminate redundant noise and extract the primary characteristics of wind speed data. In order to obtain high accuracy, and stability for wind speed prediction simultaneously, and overcome the weaknesses of single objective optimization algorithms, the optimization module of the proposed MOWOA is utilized to optimize the weights and thresholds of the Elman neutral network used in the forecasting module. Finally, the evaluation module, which includes hypothesis testing, evaluation criteria, and three experiments, is introduced perform comprehensive evaluation on the system. The results indicate that the proposed MOWOA performs better than the two recently developed MOALO and MODA algorithms, and that the proposed hybrid model outperforms all sixteen models used for comparison, which demonstrates its superior ability to generate forecasts in terms of forecasting accuracy and stability.