Chaotic wind power time series prediction via switching data-driven modes. (January 2020)

• Record Type: Journal Article
• Authors: Ouyang, Tinghui; Huang, Heming; He, Yusen; Tang, Zhenhao
• Is Part Of: Renewable energy. Volume 145(2020)
• Page Start: 270
• Page End: 281
• Publication Date: 2020-01
• Subjects: Wind power prediction -- Chaotic time series -- Markov switching regime -- Data-driven modes; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.06.047
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: To schedule wind power efficiently and to mitigate the adverse effects caused by wind's intermittency and variability, an advanced wind power prediction model is proposed in this paper. This model is a combined model via switching different data-driven chaotic time series models. First, inputs of this model come from the reconstructed data based on the chaotic characteristics of wind power time series. Second, three different data mining algorithms are used to construct wind power prediction models individually. To obtain a regime for switching optimal models, a Markov chain is trained. Then, weights of different data-driven modes are calculated by the Markov chain switching regime, and used in the final combined model for wind power prediction. The industrial data from actual wind farms is studied. Results of the proposed model are compared with that of non-reconstructed input data, traditional data-driven models and two typical combined models. These results validate the superiority of proposed model on improving wind power prediction accuracy. Highlights: Wind power prediction is realized by chaotic time series and switching regime. Chaotic wind power time series is analyzed and reconstructed as model inputs. Different data-driven models are trained as prediction modes. A regime constructed by Markov chain is proposed for modes switching.