The General Regression Neural Network Based on the Fruit Fly Optimization Algorithm and the Data Inconsistency Rate for Transmission Line Icing Prediction

Dongxiao Niu, Haichao Wang, Hanyu Chen and Yi Liang
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Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Haichao Wang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Hanyu Chen: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Yi Liang: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Energies, 2017, vol. 10, issue 12, 1-20

Abstract: Accurate and stable prediction of icing thickness on transmission lines is of great significance for ensuring the safe operation of the power grid. In order to improve the accuracy and stability of icing prediction, an innovative prediction model based on the generalized regression neural network (GRNN) and the fruit fly optimization algorithm (FOA) is proposed. Firstly, a feature selection method based on the data inconsistency rate (IR) is adopted to select the optimal feature, which aims to reduce redundant input vectors. Then, the fruit FOA is utilized for optimization of smoothing factor for the GRNN. Lastly, the icing forecasting method FOA-IR-GRNN is established. Two cases in different locations and different months are selected to validate the proposed model. The results indicate that the new hybrid FOA-IR-GRNN model presents better accuracy, robustness, and generality in icing forecasting.

Keywords: icing prediction; general regression neural network (GRNN); fruit fly optimization algorithm (FOA); data inconsistency rate (IR) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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