Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting

Yuewei Liu, Shenghui Zhang, Xuejun Chen and Jianzhou Wang
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Yuewei Liu: School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
Shenghui Zhang: School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
Xuejun Chen: Gansu Meteorogical Service Centre, Lanzhou 730020, China
Jianzhou Wang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China

Sustainability, 2018, vol. 10, issue 12, 1-30

Abstract: The use of wind power is rapidly increasing as an important part of power systems, but because of the intermittent and random nature of wind speed, system operators and researchers urgently need to find more reliable methods to forecast wind speed. Through research, it is found that the time series of wind speed demonstrate not only linear features but also nonlinear features. Hence, a combined forecasting model based on an improved cuckoo search algorithm optimizes weight, and several single models—linear model, hybrid nonlinear neural network, and fuzzy forecasting model—are developed in this paper to provide more trend change for time series of wind speed forecasting besides improving the forecasting accuracy. Furthermore, the effectiveness of the proposed model is proved by wind speed data from four wind farm sites and the results are more reliable and accurate than comparison models.

Keywords: modified cuckoo search algorithm; combined model; hybrid nonlinear models; wind speed forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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