Electricity Load Forecast using Neural Network Trained from Wavelet-Transformed Data

Djamel Benaouda, Fionn Murtagh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

With accurate electricity load forecasting important information is provided that helps to build up cost effective risk management plans for any electric utility such as electricity generators and retailers in the electricity market. In this article, we propose a wavelet based multilayer perceptron (MLPw) approach for the prediction of one-hour and one-day ahead load trained from Haar à trous wavelet-transformed historical electricity load data. We assess results produced by the MLPw method, with multiple resolution autoregressive (MAR), single resolution autoregressive (AR), multilayer perceptron (MLP), and the general regression neural network (GRNN) model. Experimental Results are based on the New South Wales (Australia) electricity load data that is provided by the National Electricity Market Management Company (NEMMCO).

Original languageEnglish
Title of host publication2006 IEEE International Conference on Engineering of Intelligent Systems (ICEIS 2006)
PublisherIEEE
Number of pages6
ISBN (Print)1424404568, 9781424404568
DOIs
Publication statusPublished - 18 Sep 2006
Externally publishedYes
EventIEEE International Conference on Engineering of Intelligent Systems - Islamabad, Pakistan
Duration: 22 Apr 200623 Apr 2006

Conference

ConferenceIEEE International Conference on Engineering of Intelligent Systems
Abbreviated titleICEIS 2006
CountryPakistan
CityIslamabad
Period22/04/0623/04/06

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Benaouda, D., & Murtagh, F. (2006). Electricity Load Forecast using Neural Network Trained from Wavelet-Transformed Data. In 2006 IEEE International Conference on Engineering of Intelligent Systems (ICEIS 2006) [1703163] IEEE. https://doi.org/10.1109/ICEIS.2006.1703163