Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal w...

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Bibliographic Details
Main Authors:	Pedro Lara-Benítez, Manuel Carranza-García, José M. Luna-Romera, José C. Riquelme
Format:	Article
Language:	English
Published:	MDPI AG 2020-03-01
Series:	Applied Sciences
Subjects:	deep learning energy demand temporal convolutional network time series forecasting
Online Access:	https://www.mdpi.com/2076-3417/10/7/2322

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