deep learning for time series forecasting the electric load case

• DOI: 10.48550/arxiv.1907.09207
• Publication Date: 2021-09-22
• Authors: Alberto Gasparin; Slobodan Lukovic; Cesare Alippi; Cesare Alippi
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; load forecasting; 0211 other engineering and technologies; Machine Learning (stat.ML); 02 engineering and technology; Machine Learning (cs.LG); QA76.75-76.765; Statistics - Machine Learning; recurrent neural nets; 0202 electrical engineering, electronic engineering, information engineering; multi-step ahead forecasting; Computer software; smart grid; electric load forecasting; power engineering computing; time-series prediction; deep learning; smart power grids; feedforward neural nets; Computational linguistics. Natural language processing; time series; P98-98.5

AbstractManagement 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.