Electricity demand forecasting 2030 by decomposition analysis of open data
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Paper number
1756
Working Group Number
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
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.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Published in
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/557
http://dx.doi.org/10.34890/780
http://dx.doi.org/10.34890/780
ISSN
2032-9644
ISBN
978-2-9602415-0-1