Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine. (1st June 2022)

• Record Type: Journal Article
• Authors: Dokur, Emrah; Erdogan, Nuh; Salari, Mahdi Ebrahimi; Karakuzu, Cihan; Murphy, Jimmy
• Is Part Of: Energy. Volume 248(2022)
• Publication Date: 2022-06-01
• Subjects: Offshore wind energy -- Wind speed forecasting -- Swarm decomposition -- Meta extreme learning machine; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.123595
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: As the share of global offshore wind energy in the electricity generation portfolio is rapidly increasing, the grid integration of large-scale offshore wind farms is becoming of interest. Due to the intermittency of wind, the stability of power systems is challenging. Therefore, accurate and fast offshore short-term wind speed forecasting tools play important role in maintaining reliability and safe operation of the power system. This paper proposes a novel hybrid offshore wind forecasting model based on swarm decomposition (SWD) and meta-extreme learning machine (Meta-ELM). This approach combines the advantages of SWD which has proven efficiency for non-stationary signals, with Meta-ELM which provides faster calculation with a lower computational burden. In order to enhance accuracy and stability, the signal is decomposed by implementing a swarm-prey hunting algorithm in SWD. To validate the model, a comparison against four conventional and state-of-the-art hybrid models is performed. The implemented models are tested on two real wind datasets. The results demonstrate that the proposed model outperforms the counterparts for all performance metrics considered. The proposed hybrid approach can also improve the performance of the Meta-ELM model as a well-known and robust method. Highlights: Based on SWD and Meta-ELM, a novel hybrid offshore wind forecasting model is proposed. The original data is decomposed by implementing a swarm-prey hunting algorithm. The Meta-ELM model is used to forecast each decomposed component. The implemented models are validated against two real-world offshore wind datasets.