dc.contributor.author | Bokolo, Anthony Junior | |
dc.contributor.author | Petersen, Sobah Abbas | |
dc.contributor.author | Ahlers, Dirk | |
dc.contributor.author | Krogstie, John | |
dc.date.accessioned | 2019-11-13T12:40:01Z | |
dc.date.available | 2019-11-13T12:40:01Z | |
dc.date.created | 2019-10-31T15:08:23Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 1478-6451 | |
dc.identifier.uri | http://hdl.handle.net/11250/2628249 | |
dc.description.abstract | The smart city has emerged as a universal term for the pervasive utilisation of information and communication technologies deployed to provide value-added services to citizens based on data generated from sectors such as energy, mobility, etc. However, current approaches are faced with interoperability as a challenging issue in processing big data. Therefore, this study explores the role of application programming interfaces (APIs) for managing real-time, online, and historical energy data in the context of residential buildings and electric vehicles. Moreover, a layered architecture that employs APIs in big data is developed for district energy management towards providing energy information intelligence and support decision-making on energy sustainability in facilitating prosumption operations. Practically, the layered architecture collects energy data and provides data to prosumers who are citizens that produce, consume, share, and sell energy generated from renewable sources such as solar and wind to better improve energy prosumption in smart grid. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Taylor & Francis | nb_NO |
dc.title | API deployment for big data management towards sustainable energy prosumption in smart cities-a layered architecture perspective | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.journal | International Journal of Sustainable Energy | nb_NO |
dc.identifier.doi | 10.1080/14786451.2019.1684287 | |
dc.identifier.cristin | 1742919 | |
dc.description.localcode | Locked until 31.10.2020 due to copyright restrictions. This is an [Accepted Manuscript] of an article published by Taylor & Francis, available at https://doi.org/10.1080/14786451.2019.1684287 | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitcode | 194,61,50,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.unitname | Institutt for arkitektur og planlegging | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |