PhD (EEE) Batch 2011 (graduated)
|Research Topic: Computational Intelligence in Offshore Wind
Research Summary: Find accurate wind speed/power forecasting methods. Pre-processing and post-processing of wind speed/power time series
- Y. Ren, P. N. Suganthan, “Empirical comparison of bagging-based ensemble classifiers”, in: 15th International Conference on Information Fusion (Fusion2012), Singapore, Jul. 2012, pp. 917–924.
- Y. Ren, P. N. Suganthan, “A kernel-ensemble bagging support vector machine”, in: 12th International Conference on Intelligent Systems Design and Applications (ISDA2012), Brunei, Oct. 2012.
- Y. Ren, P. N. Suganthan, N. Srikanth, S. Sarkar, “A hybrid ARIMA-DENFIS method for wind speed forecasting”, in: IEEE International Conference on Fuzzy Systems (Fuzzy-IEEE2013), India, Jul. 2013.
- Y. Ren, L. Zhang, P. N. Suganthan, “K-nearest neighbor based bagging SVM pruning”, in: IEEE Symposium Series on Computational Intelligence (SSCI2013), Singapore, Apr. 2013.
- L. Zhang, Y. Ren, P. N. Suganthan, “Instance based random forest with rotated feature space”, in: IEEE Symposium Series on Computational Intelligence (SSCI2013), Singapore, Apr. 2013.
- Y. Ren, P. N. Suganthan, N. Srikanth, “Wind speed forecasting: a comparison between statistical approach and learning-based approach”, in: Smart Microgrids, New Advances, Challenges and opportunities in the Actual Power Systems, US:NOVA, 2013.