Energy forecasting in the big data world

Tao Hong*, Pierre Pinson

*Corresponding author for this work

Research output: Contribution to journalEditorialResearchpeer-review

Abstract

Modern information and communication technologies have brought big data to virtually every segment of the energy and utility industries. While forecasting is an important and necessary step in the data-driven decision-making process, the problem of generating better forecasts in the world of big data is an emerging issue and a challenge to both industry and academia. This special section aims to collect top-quality forecasting articles that document cutting-edge research findings and best practices on a wide range of important business problems in the energy industry. Our emphasis is on big data, such as forecasting with high resolution data, the use of high-dimensional processes, forecasting in real-time, and the use of non-traditional data and variables.
Original languageEnglish
JournalInternational Journal of Forecasting
Volume35
Issue number4
Pages (from-to)1387-1388
ISSN0169-2070
DOIs
Publication statusPublished - 1 Jan 2019

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