Short term electrical load forecasting for mauritius using Artificial Neural Networks

Author

Bugwan, Tina ; King, Robert T F Ah

Author_Institution

Dept. of Electr. & Electron. Eng., Univ. of Mauritius, Reduit

fYear

2008

fDate

12-15 Oct. 2008

Firstpage

3668

Lastpage

3673

Abstract

The Central Electricity Board is the sole utility responsible for the generation, transmission, distribution and sale of electrical power in Mauritius. The country´s highest peak demand increased from 353.1 MW in 2005 to 367.3 MW in 2006 and corresponding annual consumptions increased from 2014.9 GWh to 2091.1 GWh and these figures are continuously increasing every year. In this paper, different Artificial Neural Network models are proposed for Short Term Load Forecasting (STLF) of the Mauritian electrical load. It is shown that models based on a combined supervised/unsupervised architecture provide better forecasting abilities compared to those relying on supervised architectures only. This is achieved by clustering of data.

Keywords

load forecasting; neural nets; power engineering computing; unsupervised learning; Central Electricity Board; Mauritian electrical load; Mauritius; artificial neural networks; highest peak demand; short term electrical load forecasting; unsupervised architecture; Artificial neural networks; Crops; Load forecasting; Power engineering and energy; Power generation; Power system modeling; Predictive models; Sugar industry; Unsupervised learning; Water storage; artificial neural networks; electrical load forecasting; supervised learning; unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on

Conference_Location

Singapore

ISSN

1062-922X

Print_ISBN

978-1-4244-2383-5

Electronic_ISBN

1062-922X

Type

conf

DOI

10.1109/ICSMC.2008.4811869

Filename

4811869