Show simple item record

dc.contributor.authorLindberg, Karen Byskov
dc.contributor.authorBakker, Steffen J.
dc.contributor.authorSartori, Igor
dc.identifier.citationUtilities Policy. 2019, 58 63-88.nb_NO
dc.description.abstractLong-term forecasts of the aggregate electric load profile are crucial for grid investment decisions and energy system planning. With current developments in energy efficiency of new and renovated buildings, and the coupling of heating and electricity demand through heat pumps, the long-term load forecast cannot be based on its historic pattern anymore. This paper presents part of an on-going work aimed at improving forecasts of the electric load profile on a national level, based on a bottom-up approach. The proposed methodology allows to account for energy efficiency measures of buildings and introduction of heat pumps on the aggregated electric load profile. Based on monitored data from over 100 non-residential buildings from all over Norway, with hourly resolution, this paper presents panel data regression models for heat load and electric specific load separately. This distinction is crucial since it allows to consider future energy efficiency measures and substitution of heating technologies. The data set is divided into 7 building types, with two variants: regular and energy efficient. The load is dependent on hour of the day, outer temperature and type of day, such as weekday and weekend. The resulting parameter estimates characterize the energy signature for each building type and variant, normalized per floor area unit (m2). Hence, it is possible to generate load profiles for typical days, weeks and years, and make aggregated load forecasts for a given area, needing only outdoor temperature and floor areas as additional data inputs.nb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleModelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecastsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.source.journalUtilities Policynb_NO
dc.description.localcode© 2019 The Authors. Published by Elsevier Ltd. This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.nb_NO
cristin.unitnameInstitutt for elkraftteknikk
cristin.unitnameInstitutt for industriell økonomi og teknologiledelse

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal