H∞ filter-based short-term electric load prediction considering characteristics of load curve

Toru Namerikawa, Yasuhiko Hosoda

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper deals with H∞ filter-based short-term electric load prediction considering characteristics of load curve. We propose a predictive method to forecast a future electric load demand for 36 hours from 0 PM, and then evaluate the peak and bottom of load curves in the next day. We propose a load model, estimates unknown parameters of model via H∞ filter using the separated data from holiday and weekday, a same pattern of the previous data have been chosen and assigned to parameters for the model. The simulation results show the effectiveness of the proposed prediction methodology.

Original languageEnglish
JournalIEEJ Transactions on Electronics, Information and Systems
Volume132
Issue number9
DOIs
Publication statusPublished - 2012

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Electric loads

Keywords

  • Characteristics of load curve
  • Electric load prediction
  • Estimation
  • H∞ filter
  • Prediction

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

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abstract = "This paper deals with H∞ filter-based short-term electric load prediction considering characteristics of load curve. We propose a predictive method to forecast a future electric load demand for 36 hours from 0 PM, and then evaluate the peak and bottom of load curves in the next day. We propose a load model, estimates unknown parameters of model via H∞ filter using the separated data from holiday and weekday, a same pattern of the previous data have been chosen and assigned to parameters for the model. The simulation results show the effectiveness of the proposed prediction methodology.",
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AB - This paper deals with H∞ filter-based short-term electric load prediction considering characteristics of load curve. We propose a predictive method to forecast a future electric load demand for 36 hours from 0 PM, and then evaluate the peak and bottom of load curves in the next day. We propose a load model, estimates unknown parameters of model via H∞ filter using the separated data from holiday and weekday, a same pattern of the previous data have been chosen and assigned to parameters for the model. The simulation results show the effectiveness of the proposed prediction methodology.

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