Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Using data from smart energy meters to gain knowledge about building clusters connected to district heating networks: A Danish example
District heating (DH) is a key element in the future 100% renewables energy grids of countries with a dominant heating season. DH is a sustainable heat supply that can use the energy flexibility of the connected buildings to perform demand-side management and balance intermittent renewables. However, to enable those strategies, plan network expansions and renovations, improve production and distribution efficiency (lower the supply temperature), and tackle distribution bottlenecks, a deeper understanding of the building stocks supplied by DH is required. Within the current context of the big data mining, the building sector is generating large data sets that can be analysed to comprehend the various energy usage profiles and characteristics of the different households and stakeholders. The recent systematic installation of smart energy meters in buildings enables the first large data analyses of city-scale clusters of buildings connected to DH networks. The current study tests different statistical and clustering analysis methods to identify the building characteristics and energy profiles of a small Danish town (1665 buildings) connected to a large DH system. An interactive web-based interface has been developed to present and share the analysis results with the professionals of the building sector and the DH utility companies. This work has been conducted with “R”, a free programming environment that is specifically well-suited for statistical analysis of large data sets.
Using data from smart energy meters to gain knowledge about building clusters connected to district heating networks: A Danish example
District heating (DH) is a key element in the future 100% renewables energy grids of countries with a dominant heating season. DH is a sustainable heat supply that can use the energy flexibility of the connected buildings to perform demand-side management and balance intermittent renewables. However, to enable those strategies, plan network expansions and renovations, improve production and distribution efficiency (lower the supply temperature), and tackle distribution bottlenecks, a deeper understanding of the building stocks supplied by DH is required. Within the current context of the big data mining, the building sector is generating large data sets that can be analysed to comprehend the various energy usage profiles and characteristics of the different households and stakeholders. The recent systematic installation of smart energy meters in buildings enables the first large data analyses of city-scale clusters of buildings connected to DH networks. The current study tests different statistical and clustering analysis methods to identify the building characteristics and energy profiles of a small Danish town (1665 buildings) connected to a large DH system. An interactive web-based interface has been developed to present and share the analysis results with the professionals of the building sector and the DH utility companies. This work has been conducted with “R”, a free programming environment that is specifically well-suited for statistical analysis of large data sets.
Using data from smart energy meters to gain knowledge about building clusters connected to district heating networks: A Danish example
Johra, Hicham (Autor:in)
01.10.2020
Johra , H 2020 , Using data from smart energy meters to gain knowledge about building clusters connected to district heating networks: A Danish example . in Book of Abstracts: 6th International Conference on Smart Energy Systems . Aalborg, Denmark , The 6th International Conference on Smart Energy Systems , Aalborg , Denmark , 06/10/2020 .
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC:
690
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