Author(s)

Ye Ren^*, P. N. Suganthan

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore City, Singapore.

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.

Wind Speed Forecasting; Empirical Mode Decomposition; k Nearest Neighbor

Ren, Y. and Suganthan, P. (2014) Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting. Journal of Power and Energy Engineering, 2, 176-185. doi: 10.4236/jpee.2014.24025.