Stacked Denoising Autoencoder network for short-term prediction of electrical Algerian load
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 th...
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| Vydáno v: | International Conference on Control, Decision and Information Technologies (Online) Ročník 1; s. 189 - 194 |
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| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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IEEE
29.06.2020
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| ISSN: | 2576-3555 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Tarek, Khadir Mohamed Belkacem, Chikhaoui Hiba, Chelabi |
| Author_xml | – sequence: 1 givenname: Chelabi surname: Hiba fullname: Hiba, Chelabi email: chelabi@labged.net organization: Université Badji Mokhtar Annaba,Laboratoire de Gestion Electronique de Documents,23000 – sequence: 2 givenname: Khadir Mohamed surname: Tarek fullname: Tarek, Khadir Mohamed email: khadir@labged.net organization: Université Badji Mokhtar Annaba,Laboratoire de Gestion Electronique de Documents,23000 – sequence: 3 givenname: Chikhaoui surname: Belkacem fullname: Belkacem, Chikhaoui email: belkacem.chikhaoui@teluq.ca organization: Technologie Université TÉLUQ 5800,LICEF Research Institute,Département Science,H2S 3L5 |
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| Snippet | 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,... |
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| StartPage | 189 |
| SubjectTerms | autoregressive variable Biological system modeling electricity consumption Forecasting Load modeling MLP Neural networks Predictive models SDAE short-term load forecasting Temperature distribution time series Time series analysis |
| Title | Stacked Denoising Autoencoder network for short-term prediction of electrical Algerian load |
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