A neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent home

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

A neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent home

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

8 Citations (Scopus)

Abstract

In this paper, a short-term home daily load forecasting realized by a neural fuzzy network (NFN) and an improved genetic algorithm (GA) is proposed. It can forecast the daily load accurately with respect to different day types and weather information. It will also be shown that the improved GA performs better than the traditional GA on some benchmark test functions. By introducing switches in the links of the neural fuzzy network, the optimal network structure can be found by the improved GA. The membership functions and the number of rules of the neural fuzzy network can be generated automatically. Simulation results for a short-term daily load forecasting in an intelligent home will be given.

Original language	English
Title of host publication	IEEE International Conference on Fuzzy Systems
Pages	1456-1459
Number of pages	4
Publication status	Published - 1 Dec 2001
Event	10th IEEE International Conference on Fuzzy Systems - Melbourne, Australia Duration: 2 Dec 2001 → 5 Dec 2001

Conference

Conference	10th IEEE International Conference on Fuzzy Systems
Country/Territory	Australia
City	Melbourne
Period	2/12/01 → 5/12/01

ASJC Scopus subject areas

Software
Safety, Risk, Reliability and Quality
Chemical Health and Safety

More information

http://hdl.handle.net/10397/1409

Cite this

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title = "A neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent home",

abstract = "In this paper, a short-term home daily load forecasting realized by a neural fuzzy network (NFN) and an improved genetic algorithm (GA) is proposed. It can forecast the daily load accurately with respect to different day types and weather information. It will also be shown that the improved GA performs better than the traditional GA on some benchmark test functions. By introducing switches in the links of the neural fuzzy network, the optimal network structure can be found by the improved GA. The membership functions and the number of rules of the neural fuzzy network can be generated automatically. Simulation results for a short-term daily load forecasting in an intelligent home will be given.",

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

year = "2001",

month = dec,

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A neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent home. / Ling, S. H.; Lam, H. K.; Leung, Hung Fat Frank et al.
IEEE International Conference on Fuzzy Systems. 2001. p. 1456-1459.

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

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AB - In this paper, a short-term home daily load forecasting realized by a neural fuzzy network (NFN) and an improved genetic algorithm (GA) is proposed. It can forecast the daily load accurately with respect to different day types and weather information. It will also be shown that the improved GA performs better than the traditional GA on some benchmark test functions. By introducing switches in the links of the neural fuzzy network, the optimal network structure can be found by the improved GA. The membership functions and the number of rules of the neural fuzzy network can be generated automatically. Simulation results for 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

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ER -

A neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent home

Abstract

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ASJC Scopus subject areas

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