Daily load forecasting with a fuzzy-input-neural network in an intelligent home

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

Daily load forecasting with a fuzzy-input-neural network in an intelligent home

S. H. Ling
, 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

5 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 fuzzy-input-neural network forecaster model is proposed. This model combines a fuzzy system and a neural network. It can forecast the daily load accurately with respect to different day types under various variables. In this model, the fuzzy system performs a preprocessing for the neural network, so that the computational demand of the neural network can be reduced. Simulation results on a daily load forecasting will be given. Comparing the proposed algorithm with that of a conventional neural network, it can be shown that the proposed algorithm produces more accurate forecasting results.

Original language	English
Title of host publication	IEEE International Conference on Fuzzy Systems
Pages	449-452
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

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

More information

http://hdl.handle.net/10397/1373

Cite this

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title = "Daily load forecasting with a fuzzy-input-neural network in an intelligent home",

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 fuzzy-input-neural network forecaster model is proposed. This model combines a fuzzy system and a neural network. It can forecast the daily load accurately with respect to different day types under various variables. In this model, the fuzzy system performs a preprocessing for the neural network, so that the computational demand of the neural network can be reduced. Simulation results on a daily load forecasting will be given. Comparing the proposed algorithm with that of a conventional neural network, it can be shown that the proposed algorithm produces more accurate forecasting results.",

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AU - Ling, S. H.

AU - Leung, Hung Fat Frank

AU - Tam, P. K S

PY - 2001/12/1

Y1 - 2001/12/1

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 fuzzy-input-neural network forecaster model is proposed. This model combines a fuzzy system and a neural network. It can forecast the daily load accurately with respect to different day types under various variables. In this model, the fuzzy system performs a preprocessing for the neural network, so that the computational demand of the neural network can be reduced. Simulation results on a daily load forecasting will be given. Comparing the proposed algorithm with that of a conventional neural network, it can be shown that the proposed algorithm produces more accurate forecasting results.

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 fuzzy-input-neural network forecaster model is proposed. This model combines a fuzzy system and a neural network. It can forecast the daily load accurately with respect to different day types under various variables. In this model, the fuzzy system performs a preprocessing for the neural network, so that the computational demand of the neural network can be reduced. Simulation results on a daily load forecasting will be given. Comparing the proposed algorithm with that of a conventional neural network, it can be shown that the proposed algorithm produces more accurate forecasting results.

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M3 - Conference article published in proceeding or book

SP - 449

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BT - IEEE International Conference on Fuzzy Systems

T2 - 10th IEEE International Conference on Fuzzy Systems

Y2 - 2 December 2001 through 5 December 2001

ER -

Daily load forecasting with a fuzzy-input-neural network in an intelligent home

Abstract

Conference

ASJC Scopus subject areas

More information

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