An innovative hybrid system for wind speed forecasting based on fuzzy preprocessing scheme and multi-objective optimization. (1st May 2019)

• Record Type: Journal Article
• Authors: Li, Chen; Zhu, Zhijie; Yang, Hufang; Li, Ranran
• Is Part Of: Energy. Volume 174(2019)
• Page Start: 1219
• Page End: 1237
• Publication Date: 2019-05-01
• Subjects: Wind speed forecasting -- Hybrid system -- Multi-objective imperialist competitive algorithm -- Fuzzy time series -- Interval forecasting; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.02.194
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Wind energy is attracting increasing attention with its sustainability and cleanliness. However, owing to the volatility and intermittency of wind speed, it is challenging to establish a scientific and reliable forecasting system. Most research has mainly been based on simple data preprocessing, single objective optimization, and point prediction, which may lead to poor forecasting performance. Hence, in this study, an innovative wind speed forecasting system is developed that incorporates effective data preprocessing and a novel algorithm. In order to alleviate the complexity and chaos of a wind speed series, a fuzzy data preprocessing scheme is designed based on "decomposition and ensemble" and a fuzzy time series. Following this, a multi-objective imperialist competitive algorithm (MOICA) is proposed and applied for optimizing an extreme learning machine (ELM), and a corresponding hybrid predictor MOICA-ELM is conducted for wind speed forecasting. For further investigation the uncertainty of wind speed, both point and interval forecasting are employed in this system. Simulation results on four wind speed datasets collected from two wind farms in China are in good accordance with the empirical data with multiple criterion and scientific evaluation; these results and show a good performance of the proposed system in terms of accuracy and stability. Highlights: A hybrid system for short-term wind speed forecasting is proposed. A data-preprocessing scheme based on ICEEMDAN and FTS is designed. A new algorithm—Multi-Objective Imperialist Competitive Algorithm (MOICA) is proposed for optimizing the system. Both deterministic and interval forecasting are employed to further mine the uncertain characteristics of wind speed.