Journal
APPLIED SCIENCES-BASEL
Volume 6, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/app6120438
Keywords
Icing forecasting; Fireworks algorithm; Least square support vector machine; Feature selection
Categories
Funding
- Natural Science Foundation of China [71471059]
- Fundamental Research Funds for the Central Universities [2015XS36]
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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 theW-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.
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