Deep Learning for Time Series Forecasting: The Electric Load Case
This work addresses the need for accurate power load forecasting in Smart Grids, but it is incremental as it primarily reviews and compares existing methods in a specific domain.
The paper tackled the lack of comprehensive comparisons among deep learning architectures for electric load forecasting by experimentally evaluating models like feedforward and recurrent neural networks on two real-world datasets for one-day-ahead prediction, achieving competitive results but without specifying concrete performance numbers.
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.