Show simple item record Sigauke, Caston Maposa, Daniel Nemukula, Murendeni Maurel 2019-08-13T09:51:06Z 2019-08-13T09:51:06Z 2018
dc.identifier.issn 1996-1073
dc.description Journal article en_US
dc.description.abstract Short-term hourly load forecasting in South Africa using additive quantile regression (AQR) models is discussed in this study. The modelling approach allows for easy interpretability and accounting for residual autocorrelation in the joint modelling of hourly electricity data. A comparative analysis is done using generalised additive models (GAMs). In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso) via hierarchical interactions. Four models considered are GAMs and AQR models with and without interactions, respectively. The AQR model with pairwise interactions was found to be the best fitting model. The forecasts from the four models were then combined using an algorithm based on the pinball loss (convex combination model) and also using quantile regression averaging (QRA). The AQR model with interactions was then compared with the convex combination and QRA models and the QRA model gave the most accurate forecasts. Except for the AQR model with interactions, the other two models (convex combination model and QRA model) gave prediction interval coverage probabilities that were valid for the 90% , 95% and the 99% prediction intervals. The QRA model had the smallest prediction interval normalised average width and prediction interval normalised average deviation. The modelling framework discussed in this paper has established that going beyond summary performance statistics in forecasting has merit as it gives more insight into the developed forecasting models. View Full-Text en_US
dc.format.extent 21 pages en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.relation.requires PDF, version 1.5 en_US
dc.subject Lasso en_US
dc.subject Load forecasting en_US
dc.subject Generalised additive models en_US
dc.subject Additive quantile regression en_US
dc.subject.lcsh Electric power systems - Load dispatching en_US
dc.title Probabilistic Hourly Loading Forecasting Using AdditiveProbabilistic Hourly Load Forecasting Using Additive Quantile Regression Models en_US
dc.type Article en_US

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