Electricity demand forecasting 2030 by decomposition analysis of open data

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CIRED 2019 - 1756.pdf (437.64 KB)

Paper number

1756

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Conference name

CIRED 2019

Conference date

3-6 June 2019

Conference location

Madrid, Spain

Peer-reviewed

Yes

Short title

Convener

Authors

Räisänen, Otto , LUT University, Finland
Haakana, Juha, LUT University, Finland
Haapaniemi, Jouni, LUT University, Finland
Lassila, Jukka , LUT University, Finland
Partanen, Jarmo, LUT University, Finland

Abstract

The demand of electrical energy in the household sectorfollowed a nearly linear growth trend for a long timemaking demand forecasting relatively simple. However, inthe last decade the growth has stalled due to energyefficiency policies, structural changes in the society andemergence of new technologies. In sparsely populatedareas the population is continually declining which affectselectrical energy consumption and increases averageconductor length per customer. These changes in theoperational environment pose challenges to demandforecasting. Historical data relating to the change factorscould be used to improve demand forecasts. This studyintroduces a method that uses decomposition and timeseriesanalysis of open data to forecast future electricalenergy demand. The method is used to forecast theelectrical energy consumption for the household sector ina group of Finnish municipalities which have a decliningpopulation.

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Publisher

AIM

Date

2019-06-03

Published in

CIRED 2019 Conference

Permanent link to this record

https://cired-repository.org/handle/20.500.12455/557
http://dx.doi.org/10.34890/780

ISSN

2032-9644

ISBN

978-2-9602415-0-1