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Guo, Z., Zhao, W., Lu, H. and Wang, J. (2012) Multi-Step Forecasting for Wind Speed Using a Modified EMD-Based Artificial Neural Network Model. Renewable Energy, Elsevier, 37, 241-249. http://dx.doi.org/10.1016/j.renene.2011.06.023

has been cited by the following article:

• TITLE: Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting

  AUTHORS: Ye Ren, P. N. Suganthan

  KEYWORDS: Wind Speed Forecasting; Empirical Mode Decomposition; k Nearest Neighbor

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.2 No.4, April 16, 2014

  ABSTRACT: Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-kNN-P that applies kNN on each decomposed intrinsic mode function (IMF) and residue for separate modelling and forecasting followed by summation and an EMD-kNN-M that forms a feature vector set from all IMFs and residue followed by a single kNN modelling and forecasting. These two configurations are compared with the persistent model and the conventional kNN model on a wind speed time series dataset from Singapore. The results show that the two EMD-kNN hybrid models have good performance for longer term forecasting and EMD-kNN-M has better performance than EMD-kNN-P for shorter term forecasting.