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Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

by Tiannan Ma *,† and Dongxiao Niu
Department of Economic and Management, North China Electric Power University, Beijing 102206, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Josep M. Guerrero
Appl. Sci. 2016, 6(12), 438; https://doi.org/10.3390/app6120438
Received: 14 September 2016 / Revised: 2 December 2016 / Accepted: 11 December 2016 / Published: 16 December 2016
Accurate forecasting of icing thickness has great significance for ensuring the security and stability of the power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on the fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of the fireworks algorithm is employed to select the proper input features with the purpose of eliminating redundant influence. In addition, the aim of the W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through simulation experiments using real-world icing data from the monitoring center of the key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting. View Full-Text
Keywords: Icing forecasting; Fireworks algorithm; Least square support vector machine; Feature selection Icing forecasting; Fireworks algorithm; Least square support vector machine; Feature selection
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Ma, T.; Niu, D. Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection. Appl. Sci. 2016, 6, 438.

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