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A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting

Haben, S. and Giasemidis, G. (2016) A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting. International Journal of Forecasting, 32 (3). pp. 1017-1022. ISSN 0169-2070

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To link to this item DOI: 10.1016/j.ijforecast.2015.11.004

Abstract/Summary

We present a model for generating probabilistic forecasts by combining kernel density estimation (KDE) and quantile regression techniques, as part of the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014. The KDE method is initially implemented with a time-decay parameter. We later improve this method by conditioning on the temperature or the period of the week variables to provide more accurate forecasts. Secondly, we develop a simple but effective quantile regression forecast. The novel aspects of our methodology are two-fold. First, we introduce symmetry into the time-decay parameter of the kernel density estimation based forecast. Secondly we combine three probabilistic forecasts with different weights for different periods of the month.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Centre for the Mathematics of Human Behaviour (CMOHB)
ID Code:72410
Publisher:Elsevier

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