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Neural networks for GEFCom2017 probabilistic load forecasting
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.ORCID iD: 0000-0003-4490-9278
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems. Loyola.Tech, Loyola Andalucia University, Seville, Spain.ORCID iD: 0000-0002-0396-3326
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems. (IRES)ORCID iD: 0000-0003-0685-0199
2019 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019.
Keywords [en]
GEFCom2017, Probabilistic load forecasting, Neural networks, Temperature scenarios, Feature selection
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-258008DOI: 10.1016/j.ijforecast.2018.09.007Scopus ID: 2-s2.0-85057737557OAI: oai:DiVA.org:kth-258008DiVA, id: diva2:1349650
Note

QC 20190916

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-10-10Bibliographically approved

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Publisher's full textScopushttps://www.sciencedirect.com/science/article/pii/S0169207018301778#!

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Dimoulkas, IliasMazidi, Peyman

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