论文信息 - A time series ensemble method to predict wind power

A time series ensemble method to predict wind power

Wind power prediction refers to an approximation of the probable production of wind turbines in the near future. We present a time series ensemble framework to predict wind power. Time series wind data is transformed using a number of complementary methods. Wind power is predicted on each transformed feature space. Predictions are aggregated using a neural network at a second stage. The proposed framework is validated on wind data obtained from ten different locations across Australia. Experimental results demonstrate that the ensemble predictor performs better than the base predictors.

Alex Stojcevski | Ashfaqur Rahman | G. M. Shafiullah | Sumaira Tasnim | Amanullah Maung Than Oo

[1] 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..

[2] Kevin N. Gurney,et al. An introduction to neural networks , 2018 .

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

[4] Divyakant Agrawal,et al. A comparison of DFT and DWT based similarity search in time-series databases , 2000, CIKM '00.

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

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

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

[8] Lindsay I. Smith,et al. A tutorial on Principal Components Analysis , 2002 .

[9] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.

[10] E C Alexopoulos,et al. Introduction to multivariate regression analysis. , 2010, Hippokratia.

[11] Ashfaqur Rahman,et al. A novel ensemble classifier approach using weak classifier learning on overlapping clusters , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).