Deep learning for time series forecasting: The electric load case. Issue 1 (22nd September 2021)

• Record Type: Journal Article
• Authors: Gasparin, Alberto; Lukovic, Slobodan; Alippi, Cesare
• Is Part Of: CAAI transactions on intelligence technology. Volume 7:Issue 1(2022)
• Page Start: 1
• Page End: 25
• Publication Date: 2021-09-22
• Subjects: deep learning -- electric load forecasting -- multi‐step ahead forecasting -- smart grid -- time‐series prediction; load forecasting -- power engineering computing -- recurrent neural nets -- smart power grids -- time series -- feedforward neural nets -- convolutional neural nets -- deep learning (artificial intelligence) -- neural net architecture; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/cit2.12060
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract: Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting, which, due to its non‐linear 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—also traditional—architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting, by contrasting deep learning architectures on short‐term forecast (one‐day‐ahead prediction). Specifically, the focus is 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.