Electricity consumption forecasting using fuzzy time series

E. Bolturk; B. Oztaysi; I. U. Sari

doi:10.1109/CINTI.2012.6496768

Electricity consumption forecasting using fuzzy time series

E. Bolturk^*
, B. Oztaysi
, I. U. Sari

^*Corresponding author for this work

Department of Industrial Engineering

Istanbul Ticaret University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Citations (Scopus)

Abstract

Electricity is an important issue in energy management. Because of characteristic of electricity's non-storability, management of electricity is very valuable process. In this study, a Turkish company's two years electricity consumption have examined in order to predict possible electricity consumption. We used fuzzy time series with Singh's Method. Further, with the same data we compared total values of three periods (Unified consumption amount) and total consumption forecasts with their actuals. Finally we decide that which way is more accurate with root-mean-square deviation evaluation.

Original language	English
Title of host publication	CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings
Pages	245-249
Number of pages	5
DOIs	https://doi.org/10.1109/CINTI.2012.6496768
Publication status	Published - 2012
Event	13th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2012 - Budapest, Hungary Duration: 20 Nov 2012 → 22 Nov 2012

Publication series

Name	CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings

Conference

Conference	13th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2012
Country/Territory	Hungary
City	Budapest
Period	20/11/12 → 22/11/12

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Electricity demand forecasting
deseasonalization
fuzzy time series

Access to Document

10.1109/CINTI.2012.6496768

Cite this

Bolturk, E., Oztaysi, B., & Sari, I. U. (2012). Electricity consumption forecasting using fuzzy time series. In CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings (pp. 245-249). Article 6496768 (CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings). https://doi.org/10.1109/CINTI.2012.6496768

@inproceedings{d4664d1b8b784d959808fd437407d99b,

title = "Electricity consumption forecasting using fuzzy time series",

abstract = "Electricity is an important issue in energy management. Because of characteristic of electricity's non-storability, management of electricity is very valuable process. In this study, a Turkish company's two years electricity consumption have examined in order to predict possible electricity consumption. We used fuzzy time series with Singh's Method. Further, with the same data we compared total values of three periods (Unified consumption amount) and total consumption forecasts with their actuals. Finally we decide that which way is more accurate with root-mean-square deviation evaluation.",

keywords = "Electricity demand forecasting, deseasonalization, fuzzy time series",

author = "E. Bolturk and B. Oztaysi and Sari, \{I. U.\}",

year = "2012",

doi = "10.1109/CINTI.2012.6496768",

language = "English",

isbn = "9781467352062",

series = "CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings",

pages = "245--249",

booktitle = "CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings",

note = "13th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2012 ; Conference date: 20-11-2012 Through 22-11-2012",

}

Bolturk, E, Oztaysi, B & Sari, IU 2012, Electricity consumption forecasting using fuzzy time series. in CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings., 6496768, CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings, pp. 245-249, 13th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2012, Budapest, Hungary, 20/11/12. https://doi.org/10.1109/CINTI.2012.6496768

Electricity consumption forecasting using fuzzy time series. / Bolturk, E.; Oztaysi, B.; Sari, I. U.
CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings. 2012. p. 245-249 6496768 (CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Electricity consumption forecasting using fuzzy time series

AU - Bolturk, E.

AU - Oztaysi, B.

AU - Sari, I. U.

PY - 2012

Y1 - 2012

N2 - Electricity is an important issue in energy management. Because of characteristic of electricity's non-storability, management of electricity is very valuable process. In this study, a Turkish company's two years electricity consumption have examined in order to predict possible electricity consumption. We used fuzzy time series with Singh's Method. Further, with the same data we compared total values of three periods (Unified consumption amount) and total consumption forecasts with their actuals. Finally we decide that which way is more accurate with root-mean-square deviation evaluation.

AB - Electricity is an important issue in energy management. Because of characteristic of electricity's non-storability, management of electricity is very valuable process. In this study, a Turkish company's two years electricity consumption have examined in order to predict possible electricity consumption. We used fuzzy time series with Singh's Method. Further, with the same data we compared total values of three periods (Unified consumption amount) and total consumption forecasts with their actuals. Finally we decide that which way is more accurate with root-mean-square deviation evaluation.

KW - Electricity demand forecasting

KW - deseasonalization

KW - fuzzy time series

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U2 - 10.1109/CINTI.2012.6496768

DO - 10.1109/CINTI.2012.6496768

M3 - Conference contribution

AN - SCOPUS:84876938818

SN - 9781467352062

T3 - CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings

SP - 245

EP - 249

BT - CINTI 2012 - 13th IEEE International Symposium on Computational Intelligence and Informatics, Proceedings

T2 - 13th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2012

Y2 - 20 November 2012 through 22 November 2012

ER -

Electricity consumption forecasting using fuzzy time series

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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