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• W4205571213 startingPage "123060" @default.
• W4205571213 abstract "The economic sector is one of the most important pillars of countries. Economic activities of industry are intimately linked with the ability to meet their needs for electricity. Therefore, electricity forecasting is a very important task. It allows for better planning and management of energy resources. Several methods have been proposed to forecast energy consumption. In this work, to predict monthly electricity consumption for the economic sector, we develop a novel approach based on ensemble learning. Our approach combines three models that proved successful in the field, namely: Long Short Term Memory and Gated Recurrent Unit neural networks, and Temporal Convolutional Networks. The experiments have been conducted with almost 2000 clients and 14 years of monthly electricity consumption from Bejaia, Algeria. The results show that the proposed ensemble models achieve better performance than both the company's requirements and the prediction of the traditional individual models. Finally, statistical tests have been carried out to prove that significance of the ensemble models developed." @default.
• W4205571213 created "2022-01-25" @default.
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• W4205571213 date "2022-03-01" @default.
• W4205571213 modified "2023-10-18" @default.
• W4205571213 title "Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market" @default.
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• W4205571213 doi "https://doi.org/10.1016/j.energy.2021.123060" @default.
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