Wind Power Prediction Using Cluster Based Ensemble Regression

Accurate prediction of wind power is of vital importance for demand management. In this paper, we adopt a cluster-based ensemble framework to predict wind power. Natural groups/clusters exist in da...

[1]  Mingjin Yan,et al.  Methods of Determining the Number of Clusters in a Data Set and a New Clustering Criterion , 2005 .

[2]  Ashfaqur Rahman,et al.  Feature weighting methods for abstract features applicable to motion based video indexing , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..

[3]  Seref Sagiroglu,et al.  Data mining and wind power prediction: A literature review , 2012 .

[4]  Keying Ye,et al.  Determining the Number of Clusters Using the Weighted Gap Statistic , 2007, Biometrics.

[5]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[6]  Maria Grazia De Giorgi,et al.  Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN) , 2014 .

[7]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[8]  Florian Metze,et al.  Extracting deep bottleneck features using stacked auto-encoders , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Ashfaqur Rahman,et al.  Novel Layered Clustering-Based Approach for Generating Ensemble of Classifiers , 2011, IEEE Transactions on Neural Networks.

[10]  Alex Stojcevski,et al.  A time series ensemble method to predict wind power , 2014, 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG).

[11]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[12]  Peng Guo,et al.  A Review of Wind Power Forecasting Models , 2011 .

[13]  Simon J. Doran,et al.  Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Daniel V. Smith,et al.  A comparison of autoencoder and statistical features for cattle behaviour classification , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[15]  Ashfaqur Rahman,et al.  Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm , 2013, Knowl. Based Syst..

[16]  Ashfaqur Rahman,et al.  Cluster-based ensemble of classifiers , 2013, Expert Syst. J. Knowl. Eng..

[17]  Ashfaqur Rahman,et al.  Cluster-Oriented Ensemble Classifier: Impact of Multicluster Characterization on Ensemble Classifier Learning , 2012, IEEE Transactions on Knowledge and Data Engineering.

[18]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..