@conference {7560,
	title = {Auto-Encoder with Neural Networks for Wind Speed Forecasting},
	booktitle = {International Conference on Communications and Electrical Engineering (ICCEE) },
	year = {2018},
	pages = {1-5},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The use of wind energy is progressively utilized to produce electrical energy. Wind energy is related to the variation of some atmospheric variables such as wind direction, wind speed, air density and atmospheric pressure. Recently, numerous methods base on Artificial Intelligence techniques to forecast wind speed have been proposed in the literature. In this paper a new artificial intelligence approach for wind speed time series forecasting is proposed, it is composed from two blocs: The first one is based on the use of a deep architecture. The Autoencoder which is a type of deep neural networks, utilized generally for Denoising, is employed to reduce the wind speed input dimensionality. In the second bloc of the proposed methodology, the Elman neural network is employed to forecast future values of wind series, it is a kind of recurrent neural networks that are very sensitive to historical variations. To evaluate our approach we used the following error indicators: Root Mean Square Error (RMSE),Mean Absolute Bias Error (MABE), Mean Absolute Percentage Error (MAPE)and the coefficient of determination (R 2 ). The obtained results are compared with those of the Extreme Learning Machine method.},
	url = {https://ieeexplore.ieee.org/abstract/document/8634551},
	author = {Hatem Mezaache and Hassen Bouzgou}
}
