Identification of ARMAX model for short term load forecasting: an evolutionary programming approach

Hong Tzer Yang, Chao Ming Huang, Ching Lien Huang

研究成果: Paper

18 引文 (Scopus)

摘要

This paper proposes a new evolutionary programming (EP) approach to identify the autoregressive moving average with exogenous variable (ARMAX) model for one day to one week ahead hourly load demand forecasts. Typically, the surface of forecasting error function possesses multiple local minimum points. Solutions of the traditional gradient search based identification technique therefore may stall at the local optimal points which lead to an inadequate model. By simulating natural evolutionary process, the EP algorithm offers the capability of converging towards the global extremum of a complex error surface. The developed EP based load forecasting algorithm is verified by using different types of data for practical Taiwan Power (Taipower) system and substation load as well as temperature values. Numerical results indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMAX model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques.

原文English
頁面325-330
頁數6
出版狀態Published - 1995 一月 1
事件Proceedings of the 1995 IEEE Power Industry Computer Application Conference - Salt Lake City, UT, USA
持續時間: 1995 五月 71995 五月 12

Other

OtherProceedings of the 1995 IEEE Power Industry Computer Application Conference
城市Salt Lake City, UT, USA
期間95-05-0795-05-12

指紋

Evolutionary algorithms
Identification (control systems)
Temperature

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering

引用此文

Yang, H. T., Huang, C. M., & Huang, C. L. (1995). Identification of ARMAX model for short term load forecasting: an evolutionary programming approach. 325-330. 論文發表於 Proceedings of the 1995 IEEE Power Industry Computer Application Conference, Salt Lake City, UT, USA, .
Yang, Hong Tzer ; Huang, Chao Ming ; Huang, Ching Lien. / Identification of ARMAX model for short term load forecasting : an evolutionary programming approach. 論文發表於 Proceedings of the 1995 IEEE Power Industry Computer Application Conference, Salt Lake City, UT, USA, .6 p.
@conference{9fad24d8c714447daa09c0a6aa126003,
title = "Identification of ARMAX model for short term load forecasting: an evolutionary programming approach",
abstract = "This paper proposes a new evolutionary programming (EP) approach to identify the autoregressive moving average with exogenous variable (ARMAX) model for one day to one week ahead hourly load demand forecasts. Typically, the surface of forecasting error function possesses multiple local minimum points. Solutions of the traditional gradient search based identification technique therefore may stall at the local optimal points which lead to an inadequate model. By simulating natural evolutionary process, the EP algorithm offers the capability of converging towards the global extremum of a complex error surface. The developed EP based load forecasting algorithm is verified by using different types of data for practical Taiwan Power (Taipower) system and substation load as well as temperature values. Numerical results indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMAX model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques.",
author = "Yang, {Hong Tzer} and Huang, {Chao Ming} and Huang, {Ching Lien}",
year = "1995",
month = "1",
day = "1",
language = "English",
pages = "325--330",
note = "Proceedings of the 1995 IEEE Power Industry Computer Application Conference ; Conference date: 07-05-1995 Through 12-05-1995",

}

Yang, HT, Huang, CM & Huang, CL 1995, 'Identification of ARMAX model for short term load forecasting: an evolutionary programming approach', 論文發表於 Proceedings of the 1995 IEEE Power Industry Computer Application Conference, Salt Lake City, UT, USA, 95-05-07 - 95-05-12 頁 325-330.

Identification of ARMAX model for short term load forecasting : an evolutionary programming approach. / Yang, Hong Tzer; Huang, Chao Ming; Huang, Ching Lien.

1995. 325-330 論文發表於 Proceedings of the 1995 IEEE Power Industry Computer Application Conference, Salt Lake City, UT, USA, .

研究成果: Paper

TY - CONF

T1 - Identification of ARMAX model for short term load forecasting

T2 - an evolutionary programming approach

AU - Yang, Hong Tzer

AU - Huang, Chao Ming

AU - Huang, Ching Lien

PY - 1995/1/1

Y1 - 1995/1/1

N2 - This paper proposes a new evolutionary programming (EP) approach to identify the autoregressive moving average with exogenous variable (ARMAX) model for one day to one week ahead hourly load demand forecasts. Typically, the surface of forecasting error function possesses multiple local minimum points. Solutions of the traditional gradient search based identification technique therefore may stall at the local optimal points which lead to an inadequate model. By simulating natural evolutionary process, the EP algorithm offers the capability of converging towards the global extremum of a complex error surface. The developed EP based load forecasting algorithm is verified by using different types of data for practical Taiwan Power (Taipower) system and substation load as well as temperature values. Numerical results indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMAX model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques.

AB - This paper proposes a new evolutionary programming (EP) approach to identify the autoregressive moving average with exogenous variable (ARMAX) model for one day to one week ahead hourly load demand forecasts. Typically, the surface of forecasting error function possesses multiple local minimum points. Solutions of the traditional gradient search based identification technique therefore may stall at the local optimal points which lead to an inadequate model. By simulating natural evolutionary process, the EP algorithm offers the capability of converging towards the global extremum of a complex error surface. The developed EP based load forecasting algorithm is verified by using different types of data for practical Taiwan Power (Taipower) system and substation load as well as temperature values. Numerical results indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMAX model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques.

UR - http://www.scopus.com/inward/record.url?scp=0029215520&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029215520&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0029215520

SP - 325

EP - 330

ER -

Yang HT, Huang CM, Huang CL. Identification of ARMAX model for short term load forecasting: an evolutionary programming approach. 1995. 論文發表於 Proceedings of the 1995 IEEE Power Industry Computer Application Conference, Salt Lake City, UT, USA, .