ELECTRICITY LOAD FORECASTING WITH ARTIFICIAL NEURAL NETWORKS

Authors

  • H. Boraine Department of Statistics, University of Pretoria, South Africa
  • V.S.S. Yadavalli Department of Industrial and Systems Engineering, University of Pretoria, South Africa

DOI:

https://doi.org/10.7166/14-2-263

Abstract

ENGLISH ABSTRACT: Artificial neural networks are powerful tools for time series forecasting. The problem addressed in this article is to do multi-step prediction of a stationary time series, and to find the associated prediction limits. Artificial neural network models for time series are non-linear. However, results that are applicable to linear models are sometimes mistakenly applied to non-linear models. One example where this is observed is in multi-step forecasting. A bootstrap method is proposed to calculate one- and multi-step predictions and prediction limits. The results are applied to an electricity load time series as well as to a pure autoregressive time series.

AFRIKAANSE OPSOMMING: Kunsmatige neurale netwerke is kragtige instrumente vir tydreeksvoorspelling. In hierdie artikel word multistap-vooruitberaming van

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Published

2011-11-05

How to Cite

Boraine, H., & Yadavalli, V. (2011). ELECTRICITY LOAD FORECASTING WITH ARTIFICIAL NEURAL NETWORKS. The South African Journal of Industrial Engineering, 14(2). https://doi.org/10.7166/14-2-263

Issue

Vol. 14 No. 2 (2003)

Section

General Articles

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