A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China

Jianzhou Wang and Shenghua Xiong

Energy, 2014, vol. 76, issue C, 526-541

Abstract: Wind energy is regarded as a worldwide renewable and alternative energy that can relieve the energy shortage, reduce environmental pollution, and provide a significant potential economic benefit. In this paper, a hybrid method is developed to properly and efficiently forecast the daily wind speed in Hainan Province, China. The proposed hybrid forecasting model consists of outlier detection and a bivariate fuzzy time series, which provides a more powerful forecasting capacity of daily wind speed than that of traditional single forecasting models. To verify the developed approach, daily wind speed data from January 2008 to December 2012 in Hainan Province, China, are used for model construction and testing. The results show that the developed hybrid forecasting model achieves high forecasting accuracy and is suitable for forecasting the wind energy of China's large wind farms.

Keywords: Outlier detection; ARMA; BPANN; Bivariate fuzzy time series (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:76:y:2014:i:c:p:526-541

DOI: 10.1016/j.energy.2014.08.064

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