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

• 作者: P. N. Suganthan Ye Ren
• 来源期刊: 电力能源(英文)
• 学科: 地球科学
• 关键词: WIND SPEED Forecasting Empirical MODE DECOMPOSITION k Nearest NEIGHBOR
• 年，卷（期）: dlnyyw_2014,（4）
• 页码范围: 176-185
• 页数: 10页
• 分类号: P4

摘要

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.