An advanced evolutionary algorithm for load forecasting with the Kalman filter

Zeke S.H. Chan; H. W. Ngan; Y. F. Fung; A. B. Rad

An advanced evolutionary algorithm for load forecasting with the Kalman filter

Zeke S.H. Chan, H. W. Ngan, Y. F. Fung, A. B. Rad

Research output: Journal article publication › Conference article › Academic research › peer-review

Abstract

In this work we design an advanced Evolutionary Algorithm for optimizing a Kalman Filter load forecasting model. The EA employs parallel architecture and an advanced mutation operator called the "Selection Follower". Its performance is benchmarked with that of the Expectation-Maximization algorithm in minimizing the mean-square-error of the KF prediction. Results show that although the EA requires more function evaluations, it outperforms the EM algorithm consistently.

Original language	English
Article number	59848
Pages (from-to)	134-138
Number of pages	5
Journal	IEE Conference Publication
Issue number	478 I
Publication status	Published - Nov 2000
Event	5th International Conference on Advances in Power System Control, Operation and Management - Tsimshatsui, Kowloon, Hong Kong Duration: 30 Oct 2000 → 1 Nov 2000

Keywords

Adaptive mutation
Evolutionary algorithm
Genetic algorithm
Kalman filter
Load forecasting

ASJC Scopus subject areas

Electrical and Electronic Engineering

Cite this

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title = "An advanced evolutionary algorithm for load forecasting with the Kalman filter",

abstract = "In this work we design an advanced Evolutionary Algorithm for optimizing a Kalman Filter load forecasting model. The EA employs parallel architecture and an advanced mutation operator called the {"}Selection Follower{"}. Its performance is benchmarked with that of the Expectation-Maximization algorithm in minimizing the mean-square-error of the KF prediction. Results show that although the EA requires more function evaluations, it outperforms the EM algorithm consistently.",

keywords = "Adaptive mutation, Evolutionary algorithm, Genetic algorithm, Kalman filter, Load forecasting",

author = "Chan, \{Zeke S.H.\} and Ngan, \{H. W.\} and Fung, \{Y. F.\} and Rad, \{A. B.\}",

year = "2000",

month = nov,

language = "English",

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journal = "IEE Conference Publication",

issn = "0537-9989",

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T1 - An advanced evolutionary algorithm for load forecasting with the Kalman filter

AU - Chan, Zeke S.H.

AU - Ngan, H. W.

AU - Fung, Y. F.

AU - Rad, A. B.

PY - 2000/11

Y1 - 2000/11

N2 - In this work we design an advanced Evolutionary Algorithm for optimizing a Kalman Filter load forecasting model. The EA employs parallel architecture and an advanced mutation operator called the "Selection Follower". Its performance is benchmarked with that of the Expectation-Maximization algorithm in minimizing the mean-square-error of the KF prediction. Results show that although the EA requires more function evaluations, it outperforms the EM algorithm consistently.

AB - In this work we design an advanced Evolutionary Algorithm for optimizing a Kalman Filter load forecasting model. The EA employs parallel architecture and an advanced mutation operator called the "Selection Follower". Its performance is benchmarked with that of the Expectation-Maximization algorithm in minimizing the mean-square-error of the KF prediction. Results show that although the EA requires more function evaluations, it outperforms the EM algorithm consistently.

KW - Adaptive mutation

KW - Evolutionary algorithm

KW - Genetic algorithm

KW - Kalman filter

KW - Load forecasting

UR - https://www.scopus.com/pages/publications/0035760911

M3 - Conference article

AN - SCOPUS:0035760911

SN - 0537-9989

SP - 134

EP - 138

JO - IEE Conference Publication

JF - IEE Conference Publication

IS - 478 I

M1 - 59848

T2 - 5th International Conference on Advances in Power System Control, Operation and Management

Y2 - 30 October 2000 through 1 November 2000

ER -

An advanced evolutionary algorithm for load forecasting with the Kalman filter

Abstract

Keywords

ASJC Scopus subject areas

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