Preprint Article Version 1 This version is not peer-reviewed

Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

Version 1 : Received: 4 March 2020 / Approved: 5 March 2020 / Online: 5 March 2020 (15:02:37 CET)

A peer-reviewed article of this Preprint also exists.

Lara-Benítez, P.; Carranza-García, M.; Luna-Romera, J.M.; Riquelme, J.C. Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting. Appl. Sci. 2020, 10, 2322. Lara-Benítez, P.; Carranza-García, M.; Luna-Romera, J.M.; Riquelme, J.C. Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting. Appl. Sci. 2020, 10, 2322.

Journal reference: Appl. Sci. 2020, 10, 2322
DOI: 10.3390/app10072322

Abstract

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 type 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 different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

Subject Areas

Deep learning; Energy demand; Temporal convolutional network; Time series forecasting

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