Demand and residual demand modelling using quantile regression

Demand and residual demand modelling using quantile regression

Residual demand, the difference between demand and solar and wind production, is an important variable in predicting the future price and storage requirements. However, little is known about predicting the residual demand itself as well as its quantiles. Therefore, we model both demand and residual...

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Main Authors: Do Linh Phuong Catherine, Hagfors Lars Ivar, Lin Kuan-Heng, Molnár Peter
Format: Article
Language:English
Published: EDP Sciences 2016-01-01
Series:Renewable Energy and Environmental Sustainability
Online Access:https://www.rees-journal.org/articles/rees/full_html/2016/01/rees160045-s/rees160045-s.html
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spelling doaj-72b36d56171b440b87ba2dc1afd52a482020-11-25T04:07:29ZengEDP SciencesRenewable Energy and Environmental Sustainability2493-94392016-01-0114110.1051/rees/2016045rees160045-sDemand and residual demand modelling using quantile regressionDo Linh Phuong Catherine0Hagfors Lars Ivar1Lin Kuan-Heng2Molnár Peter3Department of Industrial Economics and Technology Management, Norwegian University of Science and TechnologyDepartment of Industrial Economics and Technology Management, Norwegian University of Science and TechnologyCharles University, CERGE-EI (Center for Economic Research and Graduate Education – Economics Institute)Department of Industrial Economics and Technology Management, Norwegian University of Science and TechnologyResidual demand, the difference between demand and solar and wind production, is an important variable in predicting the future price and storage requirements. However, little is known about predicting the residual demand itself as well as its quantiles. Therefore, we model both demand and residual demand using both ordinary and quantile regression and compare the results for the hourly electricity consumption in Germany. We find that the residual demand is less predictable than demand. The effect is visible for all hours, and is higher for the lower than the upper quantiles.https://www.rees-journal.org/articles/rees/full_html/2016/01/rees160045-s/rees160045-s.html
collection DOAJ
language English
format Article
sources DOAJ
author Do Linh Phuong Catherine
Hagfors Lars Ivar
Lin Kuan-Heng
Molnár Peter
spellingShingle Do Linh Phuong Catherine
Hagfors Lars Ivar
Lin Kuan-Heng
Molnár Peter
Demand and residual demand modelling using quantile regression
Renewable Energy and Environmental Sustainability
author_facet Do Linh Phuong Catherine
Hagfors Lars Ivar
Lin Kuan-Heng
Molnár Peter
author_sort Do Linh Phuong Catherine
title Demand and residual demand modelling using quantile regression
title_short Demand and residual demand modelling using quantile regression
title_full Demand and residual demand modelling using quantile regression
title_fullStr Demand and residual demand modelling using quantile regression
title_full_unstemmed Demand and residual demand modelling using quantile regression
title_sort demand and residual demand modelling using quantile regression
publisher EDP Sciences
series Renewable Energy and Environmental Sustainability
issn 2493-9439
publishDate 2016-01-01
description Residual demand, the difference between demand and solar and wind production, is an important variable in predicting the future price and storage requirements. However, little is known about predicting the residual demand itself as well as its quantiles. Therefore, we model both demand and residual demand using both ordinary and quantile regression and compare the results for the hourly electricity consumption in Germany. We find that the residual demand is less predictable than demand. The effect is visible for all hours, and is higher for the lower than the upper quantiles.
url https://www.rees-journal.org/articles/rees/full_html/2016/01/rees160045-s/rees160045-s.html
work_keys_str_mv AT dolinhphuongcatherine demandandresidualdemandmodellingusingquantileregression
AT hagforslarsivar demandandresidualdemandmodellingusingquantileregression
AT linkuanheng demandandresidualdemandmodellingusingquantileregression
AT molnarpeter demandandresidualdemandmodellingusingquantileregression
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