A multimodal approach to chaotic renewable energy prediction using meteorological and historical information

• Main Authors: Goh, Hui Hwang, He, Ronghui, Zhang, Dongdong, Liu, Hui, Dai, Wei, Lim, Chee Shen, Tonni Agustiono Kurniawan, Teo, Kenneth Tze Kin, Goh, Kai Chen
• Format: Article
• Language: English; English
• Published: Elsevier 2022
• Subjects: QC851-999 Meteorology. Climatology Including the earth's atmosphere; TJ807-830 Renewable energy sources
• Online Access: https://eprints.ums.edu.my/id/eprint/32766/1/A%20multimodal%20approach%20to%20chaotic%20renewable%20energy%20prediction%20using%20meteorological%20and%20historical%20information.ABSTRACT.pdf; https://eprints.ums.edu.my/id/eprint/32766/2/A%20multimodal%20approach%20to%20chaotic%20renewable%20energy%20prediction%20using%20meteorological%20and%20historical%20information.pdf; https://eprints.ums.edu.my/id/eprint/32766/; https://www.sciencedirect.com/science/article/abs/pii/S1568494622000412; https://doi.org/10.1016/j.asoc.2022.108487

Wind energy, which exhibits non-stationarity, randomness, and intermittency, is inextricably linked to meteorological data. The wind power series can be broken down into several subsequences using data decomposition techniques to make forecasting simpler and more accurate. Because of this, a single...