Abstract:

Precise forecasting of renewable energies such as solar and wind is becoming one of the very important concerns in developing modern electrical grids. Hence, establishing appropriate tools of weather forecasting with satisfactory accuracy becomes an essential preoccupation in today's changing power world. In this paper, an approach based on Principal Component Analysis (PCA) and Extreme Learning Machines (ELM) is proposed for the forecasting of time series. The PCA maps the data into a smaller subspace in which the components accounts for as much of the variability in the input space as possible. The variables extracted by the PCA are then introduced to the extreme learning machines, a learning algorithm much faster than the traditional gradient-based learning algorithms. The experiments carried out on three time series lead to: (i) The PCA as variable selection method shows a positive impact on the accuracy of the forecasting process. (ii) ELM model is significantly faster than Multi-Layer Perceptron Network, Radial Basis Function Networks, and Least Squares Support Vector Machines, while preserving the same accuracy level.

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