Can I read Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting on EtoBox?

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting by Lara-Benítez, Pedro; Carranza-García, Manuel; Luna-Romera, José M.; Riquelme, José C. is a Engineering article available to read on EtoBox.

What is Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting about?

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 with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with di

Who reads Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting?

Author

Lara-Benítez, Pedro; Carranza-García, Manuel; Luna-Romera, José M.; Riquelme, José C.

Publisher

MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: MDPI AG, 2011- (ISSN 2076-3417)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)