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Kernel Principal Components Analysis with Extreme Learning Machines for Wind Speed Prediction

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Abstract

Nowadays, wind power and precise forecasting are of great importance for the development of modern electrical grids. In this paper we propose a prediction system for time series based on Kernel Principal Component Analysis (KPCA) and Extreme Learning Machine (ELM). To compare the proposed approach, three dimensionality reduction techniques were used: full space (50 variables), part of space (last four variables) and classical Principal Components Analysis (PCA). These models were compared using three evaluation criteria: mean absolute error (MAE), root mean square error (RMSE), and normalized mean square error (NMSE). The results show that the reduction of the original input space affects positively the prediction output of the wind speed. Thus, It can be concluded that the non linear model (KPCA) model outperform the other reduction techniques in terms of prediction performance.
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Dates and versions

hal-01394000 , version 1 (08-11-2016)

Identifiers

  • HAL Id : hal-01394000 , version 1

Cite

Hatem Mezaache, Hassen Bouzgou, Christian Raymond. Kernel Principal Components Analysis with Extreme Learning Machines for Wind Speed Prediction. Seventh International Renewable Energy Congress, IREC2016, Mar 2016, Hammamet, Tunisia. ⟨hal-01394000⟩
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