Stacked Denoising Autoencoder network for short-term prediction of electrical Algerian load [r-libre/1966]

Chelabi, Hiba; Khadir, Mohamed Tarek et Chikhaoui, Belkacem (sous presse). Stacked Denoising Autoencoder network for short-term prediction of electrical Algerian load. Dans 7th International Conference on Control, Decision and Information Technologies (CoDIT’20). IEEE.

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Catégorie de document :	Communications dans des actes de congrès/colloques
Évaluation par un comité de lecture :	Oui
Étape de publication :	Accepté (sous presse)
Résumé :	Short-term load forecasting is a topic of considerable interest; it ensures the balance between the production and consumption one day ahead. In this paper, time series models have been developed to provide an efficient forecast for electricity consumption in Algeria using Deep Neural Networks in the form of Stacked Denoising Autoencoder (SDAE) and a regular Multilayer Perceptron (MLP) as a benchmark model. The obtained models are established and evaluated using the hourly temperature and electricity consumption data provided by the Algerian National Electricity and Gas Company (SONELGAZ). Convincing forecasting results for the Algerian national load were found and conclusions drawn. Keywords: short-term load forecasting, electricity consumption, time series, autoregressive variable, MLP, SDAE.
Déposant:	Chikhaoui, Belkacem
Responsable :	Belkacem Chikhaoui
Dépôt :	06 mai 2020 20:31
Dernière modification :	06 mai 2020 20:31

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