Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models

Caston Sigauke (), Murendeni Maurel Nemukula and Daniel Maposa
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Murendeni Maurel Nemukula: Department of Statistics and Operations Research, University of Limpopo, Private Bag X1106, Sovenga 0727, South Africa
Daniel Maposa: Department of Statistics and Operations Research, University of Limpopo, Private Bag X1106, Sovenga 0727, South Africa

Energies, 2018, vol. 11, issue 9, 1-21

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.

Keywords: additive quantile regression; Lasso; load forecasting; generalised additive models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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