ELECTRICITY LOAD FORECASTING WITH ARTIFICIAL NEURAL NETWORKS
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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