Comparison of serial and parallel approaches using artificial neural networks for Algerian short-term load forecasting

KHEIR, EDDINE FARFAR and MOHAMED, AREK KHADIR and OUSSAMA, LAIB (2015) Comparison of serial and parallel approaches using artificial neural networks for Algerian short-term load forecasting. In: Third International Conference on Advances in Computing, Electronics and Electrical Technology - CEET 2015, 11-12 APRIL 2015, Kuala Lumpur, Malaysia.

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Abstract

Knowing that electrical load is a non storable resource; short term electric load forecasting becomes an important tool to optimise dispatching of electrical load in regular system planning. Several techniques have been used to accomplish this task, from traditional linear regression and Box- Jenkins to artificial intelligence approaches such as Artificial Neural Networks (ANN). This work presents a comparative study of serial and parallel ANN approaches for forecasting 168 hours ahead using a multiple linear regression model as a benchmark for comparison. The results obtained by the latter method, are compared with the ANN serial and parallel developed approaches. These models were trained solemnly on past load consumption data, given by the Algerian national electricity company. This results in Nonlinear Autoregressive Models (NAR), however once the approach validity is proven, the addition of exogenous inputs can only improve model results.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: neural network, time series, short term forecasting, multiple linear regressions
Depositing User: Mr. John Steve
Date Deposited: 07 May 2019 10:09
Last Modified: 07 May 2019 10:09
URI: http://publications.theired.org/id/eprint/1949

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