Ensemble forecasting for electricity consumption based on nonlinear optimization

Jun Hao; Qianqian Feng; Weilan Suo; Guowei Gao; Xiaolei Sun

doi:10.1016/j.procs.2019.11.252

Ensemble forecasting for electricity consumption based on nonlinear optimization

Jun Hao
, Qianqian Feng
, Weilan Suo
, Guowei Gao
, Xiaolei Sun^*

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

6 Scopus citations

Abstract

Accurate electricity power demand forecasting can provide scientific decision-making basis for policy making and planning implementation and the electricity-generating target. In this paper, a novel ensemble forecasting model with nonlinear optimization is proposed to predict the demand of electricity. The results of basic forecasting models including exponential smoothing, ARIMA, SVR and extreme learning machine are integrated. Taking clean electricity demand of world's major regions as sample, the results reveal that the ensemble approach performs much better than the single and average integrated models in terms of the accuracy.

Original language	English
Pages (from-to)	19-24
Number of pages	6
Journal	Procedia Computer Science
Volume	162
DOIs	https://doi.org/10.1016/j.procs.2019.11.252
State	Published - 2019
Externally published	Yes
Event	7th International Conference on Information Technology and Quantitative Management, ITQM 2019 - Granada, Spain Duration: 3 Nov 2019 → 6 Nov 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Electricity consumption
Ensemble forecasting
Nonlinear optimization

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10.1016/j.procs.2019.11.252

Cite this

@article{208b072e45fd4802ae90d3354cf9acc6,

title = "Ensemble forecasting for electricity consumption based on nonlinear optimization",

abstract = "Accurate electricity power demand forecasting can provide scientific decision-making basis for policy making and planning implementation and the electricity-generating target. In this paper, a novel ensemble forecasting model with nonlinear optimization is proposed to predict the demand of electricity. The results of basic forecasting models including exponential smoothing, ARIMA, SVR and extreme learning machine are integrated. Taking clean electricity demand of world's major regions as sample, the results reveal that the ensemble approach performs much better than the single and average integrated models in terms of the accuracy.",

keywords = "Electricity consumption, Ensemble forecasting, Nonlinear optimization",

author = "Jun Hao and Qianqian Feng and Weilan Suo and Guowei Gao and Xiaolei Sun",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors. Published by Elsevier B.V.; 7th International Conference on Information Technology and Quantitative Management, ITQM 2019 ; Conference date: 03-11-2019 Through 06-11-2019",

year = "2019",

doi = "10.1016/j.procs.2019.11.252",

language = "英语",

volume = "162",

pages = "19--24",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Ensemble forecasting for electricity consumption based on nonlinear optimization

AU - Hao, Jun

AU - Feng, Qianqian

AU - Suo, Weilan

AU - Gao, Guowei

AU - Sun, Xiaolei

PY - 2019

Y1 - 2019

N2 - Accurate electricity power demand forecasting can provide scientific decision-making basis for policy making and planning implementation and the electricity-generating target. In this paper, a novel ensemble forecasting model with nonlinear optimization is proposed to predict the demand of electricity. The results of basic forecasting models including exponential smoothing, ARIMA, SVR and extreme learning machine are integrated. Taking clean electricity demand of world's major regions as sample, the results reveal that the ensemble approach performs much better than the single and average integrated models in terms of the accuracy.

AB - Accurate electricity power demand forecasting can provide scientific decision-making basis for policy making and planning implementation and the electricity-generating target. In this paper, a novel ensemble forecasting model with nonlinear optimization is proposed to predict the demand of electricity. The results of basic forecasting models including exponential smoothing, ARIMA, SVR and extreme learning machine are integrated. Taking clean electricity demand of world's major regions as sample, the results reveal that the ensemble approach performs much better than the single and average integrated models in terms of the accuracy.

KW - Electricity consumption

KW - Ensemble forecasting

KW - Nonlinear optimization

UR - https://www.scopus.com/pages/publications/85081054701

U2 - 10.1016/j.procs.2019.11.252

DO - 10.1016/j.procs.2019.11.252

M3 - 会议文章

AN - SCOPUS:85081054701

SN - 1877-0509

VL - 162

SP - 19

EP - 24

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 7th International Conference on Information Technology and Quantitative Management, ITQM 2019

Y2 - 3 November 2019 through 6 November 2019

ER -

Ensemble forecasting for electricity consumption based on nonlinear optimization

Abstract

UN SDGs

Keywords

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