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Energies 2017, 10(12), 2066; doi:10.3390/en10122066

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

School of Economics and Management, North China Electric Power University, Beijing 102206, China
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Received: 26 October 2017 / Revised: 28 November 2017 / Accepted: 29 November 2017 / Published: 5 December 2017
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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. View Full-Text
Keywords: icing prediction; general regression neural network (GRNN); fruit fly optimization algorithm (FOA); data inconsistency rate (IR) icing prediction; general regression neural network (GRNN); fruit fly optimization algorithm (FOA); data inconsistency rate (IR)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Niu, D.; Wang, H.; Chen, H.; Liang, Y. The General Regression Neural Network Based on the Fruit Fly Optimization Algorithm and the Data Inconsistency Rate for Transmission Line Icing Prediction. Energies 2017, 10, 2066.

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