A hybrid dynamic and fuzzy time series model for mid-term power load forecasting

Woo Joo Lee, Jinkyu Hong

Research output: Contribution to journalArticlepeer-review

89 Citations (Scopus)

Abstract

A new hybrid model for forecasting the electric power load several months ahead is proposed. To allow for distinct responses from individual load sectors, this hybrid model, which combines dynamic (i.e., air temperature dependency of power load) and fuzzy time series approaches, is applied separately to the household, public, service, and industrial sectors. The hybrid model is tested using actual load data from the Seoul metropolitan area, and its predictions are compared with those from two typical dynamic models. Our investigation shows that, in the case of four-month forecasting, the proposed model gives the actual monthly power load of every sector with only less than 3% absolute error and satisfactory reduction of forecasting errors compared to other models from previous studies.

Original languageEnglish
Pages (from-to)1057-1062
Number of pages6
JournalInternational Journal of Electrical Power and Energy Systems
Volume64
DOIs
Publication statusPublished - 2015 Jan

Bibliographical note

Funding Information:
This study was supported by the Korea Meteorological Administration Research and Development Program under Grant CATER2012-3035 and 3055 .

Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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