A novel GA-based neural network for short-term load forecasting

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

A novel GA-based neural network for short-term load forecasting

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

6 Citations (Scopus)

Abstract

This paper presents a GA-based neural network with a novel neuron model. In this model, the neuron has two activation transfer functions and exhibits a node-by-node relationship in the hidden layer. This neural network provides a better performance than a traditional feed-forward neural network and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by GA with arithmetic crossover and non-uniform mutation. An application on short-term load forecasting is given to show the merits of the proposed neural network.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Pages	2761-2766
Number of pages	6
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/10397/1400

Cite this

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title = "A novel GA-based neural network for short-term load forecasting",

abstract = "This paper presents a GA-based neural network with a novel neuron model. In this model, the neuron has two activation transfer functions and exhibits a node-by-node relationship in the hidden layer. This neural network provides a better performance than a traditional feed-forward neural network and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by GA with arithmetic crossover and non-uniform mutation. An application on short-term load forecasting is given to show the merits of the proposed neural network.",

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N2 - This paper presents a GA-based neural network with a novel neuron model. In this model, the neuron has two activation transfer functions and exhibits a node-by-node relationship in the hidden layer. This neural network provides a better performance than a traditional feed-forward neural network and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by GA with arithmetic crossover and non-uniform mutation. An application on short-term load forecasting is given to show the merits of the proposed neural network.

AB - This paper presents a GA-based neural network with a novel neuron model. In this model, the neuron has two activation transfer functions and exhibits a node-by-node relationship in the hidden layer. This neural network provides a better performance than a traditional feed-forward neural network and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by GA with arithmetic crossover and non-uniform mutation. An application on short-term load forecasting is given to show the merits of the proposed neural network.

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M3 - Conference article published in proceeding or book

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ER -

A novel GA-based neural network for short-term load forecasting

Abstract

Conference

ASJC Scopus subject areas

More information

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