Probabilistic electric load forecasting: A tutorial review

Tao Hong, Shu Fan

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Load forecasting has been a fundamental business problem since the inception of the electric power industry. Over the past 100 plus years, both research efforts and industry practices in this area have focused primarily on point load forecasting. In the most recent decade, though, the increased market competition, aging infrastructure and renewable integration requirements mean that probabilistic load forecasting has become more and more important to energy systems planning and operations. This paper offers a tutorial review
of probabilistic electric load forecasting, including notable techniques, methodologies and evaluation methods, and common misunderstandings. We also underline the need to invest in additional research, such as reproducible case studies, probabilistic load forecast evaluation and valuation, and a consideration of emerging technologies and energy policies in the probabilistic load forecasting process. © 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)914-938
Number of pages25
JournalInternational Journal of Forecasting
Volume32
Issue number3
DOIs
Publication statusPublished - 2016

Keywords

  • Short term load forecasting
  • Long term load forecasting
  • Probabilistic load forecasting
  • Regression analysis
  • Artificial neural networks
  • Forecast evaluation

Cite this

Hong, Tao ; Fan, Shu. / Probabilistic electric load forecasting : A tutorial review. In: International Journal of Forecasting. 2016 ; Vol. 32, No. 3. pp. 914-938.
@article{e7d2b53f0b7946b5be3ee96ce4f0cb4a,
title = "Probabilistic electric load forecasting: A tutorial review",
abstract = "Load forecasting has been a fundamental business problem since the inception of the electric power industry. Over the past 100 plus years, both research efforts and industry practices in this area have focused primarily on point load forecasting. In the most recent decade, though, the increased market competition, aging infrastructure and renewable integration requirements mean that probabilistic load forecasting has become more and more important to energy systems planning and operations. This paper offers a tutorial reviewof probabilistic electric load forecasting, including notable techniques, methodologies and evaluation methods, and common misunderstandings. We also underline the need to invest in additional research, such as reproducible case studies, probabilistic load forecast evaluation and valuation, and a consideration of emerging technologies and energy policies in the probabilistic load forecasting process. {\circledC} 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.",
keywords = "Short term load forecasting, Long term load forecasting, Probabilistic load forecasting, Regression analysis, Artificial neural networks, Forecast evaluation",
author = "Tao Hong and Shu Fan",
year = "2016",
doi = "10.1016/j.ijforecast.2015.11.011",
language = "English",
volume = "32",
pages = "914--938",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier",
number = "3",

}

Probabilistic electric load forecasting : A tutorial review. / Hong, Tao; Fan, Shu.

In: International Journal of Forecasting, Vol. 32, No. 3, 2016, p. 914-938.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Probabilistic electric load forecasting

T2 - A tutorial review

AU - Hong, Tao

AU - Fan, Shu

PY - 2016

Y1 - 2016

N2 - Load forecasting has been a fundamental business problem since the inception of the electric power industry. Over the past 100 plus years, both research efforts and industry practices in this area have focused primarily on point load forecasting. In the most recent decade, though, the increased market competition, aging infrastructure and renewable integration requirements mean that probabilistic load forecasting has become more and more important to energy systems planning and operations. This paper offers a tutorial reviewof probabilistic electric load forecasting, including notable techniques, methodologies and evaluation methods, and common misunderstandings. We also underline the need to invest in additional research, such as reproducible case studies, probabilistic load forecast evaluation and valuation, and a consideration of emerging technologies and energy policies in the probabilistic load forecasting process. © 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

AB - Load forecasting has been a fundamental business problem since the inception of the electric power industry. Over the past 100 plus years, both research efforts and industry practices in this area have focused primarily on point load forecasting. In the most recent decade, though, the increased market competition, aging infrastructure and renewable integration requirements mean that probabilistic load forecasting has become more and more important to energy systems planning and operations. This paper offers a tutorial reviewof probabilistic electric load forecasting, including notable techniques, methodologies and evaluation methods, and common misunderstandings. We also underline the need to invest in additional research, such as reproducible case studies, probabilistic load forecast evaluation and valuation, and a consideration of emerging technologies and energy policies in the probabilistic load forecasting process. © 2015 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

KW - Short term load forecasting

KW - Long term load forecasting

KW - Probabilistic load forecasting

KW - Regression analysis

KW - Artificial neural networks

KW - Forecast evaluation

U2 - 10.1016/j.ijforecast.2015.11.011

DO - 10.1016/j.ijforecast.2015.11.011

M3 - Article

VL - 32

SP - 914

EP - 938

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 3

ER -