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

Cite this

@article{5239864003c545188818b8820fdbf8b0,
title = "Energy forecasting in the big data world",
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.",
author = "Tao Hong and Pierre Pinson",
year = "2019",
month = "1",
day = "1",
doi = "10.1016/j.ijforecast.2019.05.004",
language = "English",
volume = "35",
pages = "1387--1388",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier",
number = "4",

}

Energy forecasting in the big data world. / Hong, Tao; Pinson, Pierre.

In: International Journal of Forecasting, Vol. 35, No. 4, 01.01.2019, p. 1387-1388.

Research output: Contribution to journalEditorialResearchpeer-review

TY - JOUR

T1 - Energy forecasting in the big data world

AU - Hong, Tao

AU - Pinson, Pierre

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

U2 - 10.1016/j.ijforecast.2019.05.004

DO - 10.1016/j.ijforecast.2019.05.004

M3 - Editorial

VL - 35

SP - 1387

EP - 1388

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 4

ER -