Open Access
MATEC Web Conf.
Volume 70, 2016
2016 The 3rd International Conference on Manufacturing and Industrial Technologies
Article Number 10002
Number of page(s) 5
Section Electronics and Power Systems
Published online 11 August 2016
  1. M. A. Islam, et al., Global renewable energy-based electricity generation and smart grid system for energy security, Scientific World Journal, 2014:197136, (2014) [Google Scholar]
  2. E. Pelikán, et al., Wind power forecasting by an empirical model using NWP outputs, in Environment and Electrical Engineering (EEEIC), 2010 9th International Conference on, IEEE: Prague, Czech Republic, (2010) 45 [Google Scholar]
  3. S. Al-Yahyai, A. Gastli, Y. Charabi, Probabilistic wind speed forecast for wind power prediction using pseudo ensemble approach, in Power and Energy (PECon), 2012 IEEE International Conference on, IEEE: Kota Kinabalu, (2012) 127 [Google Scholar]
  4. S. Buhan, I. Cadirci, Multi-Stage Wind-Electric Power Forecast by Using a Combination of Advanced Statistical Methods. Industrial Informatics, IEEE Transactions on. (99):1, (2015) [Google Scholar]
  5. N. O. Jensen. A note on wind generator interaction, (1983) [Google Scholar]
  6. J. Taylor, P. McSharry, Short-term load forecasting methods: Anevaluation based on European data, IEEE Trans. Power Syst., 22(4):2213–2219, (Nov. 2007) [Google Scholar]
  7. H. Quan, D. Srinivasan, A. Khosravi, Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE Trans. Neural. Netw. Learn. Syst., 25(2):303–15, (2014) [Google Scholar]
  8. J. Wen, et al., Short-term wind power forecasting based on lifting wavelet transform and SVM, in Power Engineering and Automation Conference (PEAM), 2012 IEEE, IEEE: Wuhan, (2012) 1 [Google Scholar]
  9. H. Quan, D. Srinivasan, A. Khosravi, Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals, IEEE Trans. Neural. Netw. Learn. Syst., (2014) [Google Scholar]
  10. Y. Bashon, D. Neagu, M. J. Ridley, Fuzzy set-theoretical approach for comparing objects with fuzzy attributes, in Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on, IEEE: Cordoba, (2011) 754 [Google Scholar]
  11. A. Tversky, Features of Similarity, Psychological Review, 84(4):327–352, (1977) [CrossRef] [Google Scholar]
  12. Y. Wang, S. Wang, N. Zhang, A novel wind speed forecasting method based on ensemble empirical mode decomposition and GA-BP neural network, in Power and Energy Society General Meeting (PES), 2013 IEEE, IEEE: Vancouver, BC, (2013) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.