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All issues Volume 70 (2016) MATEC Web Conf., 70 (2016) 10010 References
Open Access
Issue
MATEC Web Conf.
Volume 70, 2016
2016 The 3rd International Conference on Manufacturing and Industrial Technologies
Article Number 10010
Number of page(s) 5
Section Electronics and Power Systems
DOI https://doi.org/10.1051/matecconf/20167010010
Published online 11 August 2016
  1. J. Wang, L. Li, D. Niu, and Z. Tan, An annual load forecasting model based on support vector regression with differential evolution algorithm, Applied Energy, vol. 94, (2012). [Google Scholar]
  2. H. X. Zhao and F. Magoulès, A review on the prediction of building energy consumption, Renewable and Sustainable Energy Reviews, vol. 16, pp. 3586–3592, (2012). [CrossRef] [Google Scholar]
  3. L. Suganthi and Anand A. Samuel, Energy Models for Demand Forecasting-A Review, Renewable and Sustainable Energy Reviews, vol. 16, pp. 1223–1420, (2012). [CrossRef] [Google Scholar]
  4. K. Metaxiotis, A. Kagiannas, D. Askounis, and J. Psarras, Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher, Energy Conversion and Management, vol. 44, pp. 1525–1534, (2003). [CrossRef] [Google Scholar]
  5. E. Avci, Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm–support vector machines, Expert Systems with Applications, vol. 36, pp. 1391–1402, (2009). [CrossRef] [Google Scholar]
  6. B. Dong, C. Cao, and S. E. Lee, Applying support vector machines to predict building energy consumption in tropical region, Energy and Buildings, vol. 37, pp. 545–553, 5//(2005). [CrossRef] [Google Scholar]
  7. Zhijian Hou and Z. Lian, An Application of Support Vector Machines in Cooling Load Prediction, presented at the International Systems and Application 2009, Wuhan, (2009). [Google Scholar]
  8. Z. Mustaffa, Y. Yusof, and S. S. Kamaruddin, Application of LSSVM by ABC in Energy Commodity Price Forecasting, in Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International, Langkawi, (2014), pp. 94–98. [Google Scholar]
  9. X. Yang, Comparison of the LS-SVM Based Load Forecasting Models, in IEEE, ed. Harbin, Heilongjiang, China: IEEE, (2011), pp. 2942–2945. [Google Scholar]
  10. O. Hegazy, O. S. Soliman, and M. A. Salam, LSSVM-ABC Algorithm for Stock Price prediction, International Journal of Computer Trends and Technology (IJCTT ), vol. 7, pp. 81–92, (2014). [CrossRef] [Google Scholar]
  11. R. Samsudin, P. Saad, and A. Shabri, River flow time series using least squares support vector machines, Hydrology and Earth System Sciences, vol. 15, pp. 1835–1852, (2011). [Google Scholar]
  12. Hongya Xu, Yao Dong, Jie Wu, and Weigang Zhao, Application of GMDH to Short Term Load Forecasting, Advanced in Intelligent System, vol. 138, pp. 27–32, (2012). [CrossRef] [Google Scholar]
  13. T. Jacob, U. A. Usman, S. Bemdoo, and A. A. Susan, Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network, Journal of Electrical and Electronic Engineering, vol. 3, pp. 42–47, (2015). [CrossRef] [Google Scholar]
  14. W.-C. Hong, Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm, Energy, vol. 36, pp. 5568–5578, 9// (2011). [CrossRef] [Google Scholar]
  15. Z. Mustaffa, Y. Yusof, and S. S. Kamaruddin, Application of LSSVM by ABC in energy commodity price forecasting, in Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International, (2014), pp. 94–98. [Google Scholar]
  16. D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization, vol. 39, pp. 459–471, 2007/11/01 (2007). [Google Scholar]

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