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EEM 2017 Forecast Competition: Wind power generation prediction using autoregressive models
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. (PSOP)
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. (IRES)ORCID iD: 0000-0003-0685-0199
2017 (English)Conference paper, (Refereed)
Abstract [en]

Energy forecasting provides essential contribution tointegrate renewable energy sources into power systems. Today,renewable energy from wind power is one of the fastest growingmeans of power generation. As wind power forecast accuracygains growing significance, the number of models used forforecasting is increasing as well. In this paper, we propose anautoregressive (AR) model that can be used as a benchmarkmodel to validate and rank different forecasting models andtheir accuracy. The presented paper and research was developedwithin the scope of the European energy market (EEM) 2017wind power forecasting competition.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2017.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-209946OAI: oai:DiVA.org:kth-209946DiVA: diva2:1115478
Conference
14th International Conference on the European Energy Market – EEM 2017,Dresden from 6 - 9 June 2017
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage
Available from: 2017-06-26 Created: 2017-06-26 Last updated: 2017-07-04

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