Title Neural networks for GEFCom2017 probabilistic load forecasting
Authors Dimoulkas I., MAZIDI, PEYMAN, Herre L., MAZIDI, PEYMAN
External publication No
Means Int. J. Forecast.
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 2.82500
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057737557&doi=10.1016%2fj.ijforecast.2018.09.007&partnerID=40&md5=4b6e1bad467fc45ef1a551a95491886c
Publication date 01/01/2019
ISI 000490649500017
Scopus Id 2-s2.0-85057737557
DOI 10.1016/j.ijforecast.2018.09.007
Abstract This report describes the forecasting model which was developed by team “4C” for the global energy forecasting competition 2017 (GEFCom2017), with some modifications added afterwards to improve its accuracy. The model is based on neural networks. Temperature scenarios obtained from historical data are used as inputs to the neural networks in order to create load scenarios, and these load scenarios are then transformed into quantiles. By using a feature selection approach that is based on a stepwise regression technique, a neural network based model is developed for each zone. Furthermore, a dynamic choice of the temperature scenarios is suggested. The feature selection and dynamic choice of the temperature scenarios can improve the quantile scores considerably, resulting in very accurate forecasts among the top teams. © 2018 International Institute of Forecasters
Keywords Feature selection; GEFCom2017; Neural networks; Probabilistic load forecasting; Temperature scenarios
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