# Deep Learning for Time Series Forecasting: The Electric Load Case

## TL;DR

This paper reviews and experimentally compares various deep learning architectures for short-term electric load forecasting, highlighting their performance on real-world datasets to advance the field.

## Contribution

It provides a comprehensive comparison of recent deep learning models for electric load forecasting, including architectures not previously explored in this context.

## Key findings

- Recurrent neural networks outperform feedforward models in accuracy.
- Temporal convolutional networks show promising results for short-term forecasting.
- Architectural variants improve prediction performance over baseline models.

## Abstract

Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating on two real-world datasets the most recent trends in electric load forecasting, by contrasting deep learning architectures on short term forecast (one day ahead prediction). Specifically, we focus on feedforward and recurrent neural networks, sequence to sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one.