A data set of a Norwegian energy community

This paper presents a data set designed to represent Norwegian energy communities. As such it includes household consumption data collected from smart meter measurements and divided into consumer groups, appliance consumption data collected from Norwegian households, electric vehicle data regarding...

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Main Authors: Kjersti Berg, Markus Löschenbrand
Format: Article
Language:English
Published: Elsevier 2022-02-01
Series:Data in Brief
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340921009586
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author Kjersti Berg
Markus Löschenbrand
author_facet Kjersti Berg
Markus Löschenbrand
author_sort Kjersti Berg
collection DOAJ
description This paper presents a data set designed to represent Norwegian energy communities. As such it includes household consumption data collected from smart meter measurements and divided into consumer groups, appliance consumption data collected from Norwegian households, electric vehicle data regarding charging patterns, simulated photovoltaic power generation data based on temperature and irradiance data sets and wholesale electricity prices.All data sets are further filtered by season, weekday/weekend and time segment, and then fitted to either a normal, exponential or log-normal distribution. The reason for this specific segmentation is the intention to provide a suitable data set for case studies and experiments on energy communities that consider uncertainty, a main challenge to be overcome in the practical implementation of energy community projects. In addition to this filtered version, the previously unpublished raw data sets on household consumption and photovoltaic power generation are also provided.
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spelling doaj.art-c3cb18c2a64c44ddbb4e1d678609761f2022-12-21T17:24:32ZengElsevierData in Brief2352-34092022-02-0140107683A data set of a Norwegian energy communityKjersti Berg0Markus Löschenbrand1Corresponding author.; Dept. of Electric Power Engineering, NTNU, O.S. Bragstads plass 2E, Trondheim 7034, Norway; Dept. of Energy Systems, SINTEF Energy Research, Sem Sælands vei 11, Trondheim 7034, NorwayDept. of Energy Systems, SINTEF Energy Research, Sem Sælands vei 11, Trondheim 7034, NorwayThis paper presents a data set designed to represent Norwegian energy communities. As such it includes household consumption data collected from smart meter measurements and divided into consumer groups, appliance consumption data collected from Norwegian households, electric vehicle data regarding charging patterns, simulated photovoltaic power generation data based on temperature and irradiance data sets and wholesale electricity prices.All data sets are further filtered by season, weekday/weekend and time segment, and then fitted to either a normal, exponential or log-normal distribution. The reason for this specific segmentation is the intention to provide a suitable data set for case studies and experiments on energy communities that consider uncertainty, a main challenge to be overcome in the practical implementation of energy community projects. In addition to this filtered version, the previously unpublished raw data sets on household consumption and photovoltaic power generation are also provided.http://www.sciencedirect.com/science/article/pii/S2352340921009586Energy communityElectric vehiclesElectrical power systemHousehold appliancesLocal energy communityPhotovoltaics
spellingShingle Kjersti Berg
Markus Löschenbrand
A data set of a Norwegian energy community
Data in Brief
Energy community
Electric vehicles
Electrical power system
Household appliances
Local energy community
Photovoltaics
title A data set of a Norwegian energy community
title_full A data set of a Norwegian energy community
title_fullStr A data set of a Norwegian energy community
title_full_unstemmed A data set of a Norwegian energy community
title_short A data set of a Norwegian energy community
title_sort data set of a norwegian energy community
topic Energy community
Electric vehicles
Electrical power system
Household appliances
Local energy community
Photovoltaics
url http://www.sciencedirect.com/science/article/pii/S2352340921009586
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