Short-term daily load forecasting in an intelligent home with GA-based neural network

S. H. Ling; Hung Fat Frank Leung; H. K. Lam; P. K S Tam

Short-term daily load forecasting in an intelligent home with GA-based neural network

S. H. Ling, Hung Fat Frank Leung, H. K. Lam, P. K S Tam

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

6 Citations (Scopus)

Abstract

Daily load forecasting is essential to improve the reliability of the AC power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term daily load forecasting realized by a GA-based neural network is proposed. A neural network with a switch introduced to each link is employed to minimize forecasting errors and forecast the daily load with respect to different day types and weather information. Genetic algorithm (GA) with arithmetic crossover and non-uniform mutation is used to learn the input-output relationships of an application and the optimal network structure. Simulation results on a short-term daily load forecasting in an intelligent home will be given.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Pages	997-1001
Number of pages	5
Publication status	Published - 1 Jan 2002
Event	2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: 12 May 2002 → 17 May 2002

Conference

Conference	2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/Territory	United States
City	Honolulu, HI
Period	12/05/02 → 17/05/02

ASJC Scopus subject areas

Software
Artificial Intelligence

More information

http://hdl.handle.net/10397/1407

Cite this

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title = "Short-term daily load forecasting in an intelligent home with GA-based neural network",

abstract = "Daily load forecasting is essential to improve the reliability of the AC power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term daily load forecasting realized by a GA-based neural network is proposed. A neural network with a switch introduced to each link is employed to minimize forecasting errors and forecast the daily load with respect to different day types and weather information. Genetic algorithm (GA) with arithmetic crossover and non-uniform mutation is used to learn the input-output relationships of an application and the optimal network structure. Simulation results on a short-term daily load forecasting in an intelligent home will be given.",

author = "Ling, {S. H.} and Leung, {Hung Fat Frank} and Lam, {H. K.} and Tam, {P. K S}",

year = "2002",

month = jan,

day = "1",

language = "English",

pages = "997--1001",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

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Short-term daily load forecasting in an intelligent home with GA-based neural network. / Ling, S. H.; Leung, Hung Fat Frank; Lam, H. K. et al.
Proceedings of the International Joint Conference on Neural Networks. 2002. p. 997-1001.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Leung, Hung Fat Frank

AU - Lam, H. K.

AU - Tam, P. K S

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N2 - Daily load forecasting is essential to improve the reliability of the AC power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term daily load forecasting realized by a GA-based neural network is proposed. A neural network with a switch introduced to each link is employed to minimize forecasting errors and forecast the daily load with respect to different day types and weather information. Genetic algorithm (GA) with arithmetic crossover and non-uniform mutation is used to learn the input-output relationships of an application and the optimal network structure. Simulation results on a short-term daily load forecasting in an intelligent home will be given.

AB - Daily load forecasting is essential to improve the reliability of the AC power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term daily load forecasting realized by a GA-based neural network is proposed. A neural network with a switch introduced to each link is employed to minimize forecasting errors and forecast the daily load with respect to different day types and weather information. Genetic algorithm (GA) with arithmetic crossover and non-uniform mutation is used to learn the input-output relationships of an application and the optimal network structure. Simulation results on a short-term daily load forecasting in an intelligent home will be given.

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M3 - Conference article published in proceeding or book

SP - 997

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ER -

Short-term daily load forecasting in an intelligent home with GA-based neural network

Abstract

Conference

ASJC Scopus subject areas

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