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

Energies

Dongxiao Niu¹  Haichao Wang¹  Yi Liang¹  Hanyu Chen¹ 

[1] School of Economics and Management, North China Electric Power University, Beijing 102206, China;

关键词: icing prediction;    general regression neural network (GRNN);    fruit fly optimization algorithm (FOA);    data inconsistency rate (IR);   

DOI  :  10.3390/en10122066

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown