Sammendrag
How should sustainable neighbourhoods be designed to reduce greenhouse gas emissions towards zero? What kind of information does decision makers need to make solid future plans on the neighbourhood level? A detailed understanding of a building stock’s characteristics and development over time is an underlying premise for reliable long-term building stock energy analyses. On the neighbourhood level, the building stock can be studied in large detail. Interactions between buildings and the local energy system can be analysed considering energy need, supply, local generation and local storage. Hourly resolution is needed to estimate peak heat and electricity loads in the neighbourhood. Further, greenhouse-gas (GHG) emissions resulting from the energy use in the buildings in the neighbourhood can be estimated by use of carbon intensities for the various energy carriers used in the neighbourhood.
This report is deliverable D1.2.2 and a part of FME ZEN Work Package 1 Analytic framework for design and planning of zero emission neighbourhoods (ZEN). The goal for WP 1 is to develop definitions, targets and benchmarking for ZEN, based on customized indicators and quantitative and qualitative data. Additionally, life cycle assessment methodology for energy and emissions at neighbourhood scale will be developed, as well as a citizen-centred architectural and urban toolbox for design and planning of ZEN.
A dynamic building stock model has been developed for energy- and GHG-emission scenario analyses of neighbourhoods. The model is generic and flexible and can be used to model any neighbourhood where building stock data is available. It makes use of a description of the current stock, as well as plans for construction, demolition and renovation activities in the neighbourhood. If plans are not available, the model may simulate stock activities by use of probability distributions. The neighbourhood building stock is segmented by use of archetypes defined by the buildings’ age, renovation state and floor area classes. Examples are grouping the two floor area types single family houses (SFH) and terraced houses (TH) together into a detached dwellings floor area class or grouping primary schools and secondary schools into a floor area class called “school buildings”. Hourly energy demand is estimated using delivered energy intensity profiles given for different archetypes of buildings or empirical data. Any number of different energy carriers and purposes can be defined and monthly or yearly carbon emission intensities can be given for each individual carrier. This serves as a basis to estimate hourly, monthly or yearly delivered energy and GHG emissions for a given neighbourhood under study.
Two cases are analysed in this report: i) a hypothetical case of an imaginary neighbourhood consisting of apartment block (AB) and SFH dwellings, and ii) the Gløshaugen campus of the Norwegian University of Science and Technology (NTNU). Gløshaugen campus is a neighbourhood that has a high complexity of floor area types and usage. The purpose of the two very different case studies is not to provide reliable case studies at present, but to demonstrate how the model is capable of long-term analyses of both homogenous and complex neighbourhoods in order to offer detailed understanding of possible future hourly energy use and GHG emissions.
This report is deliverable D1.2.2 and a part of FME ZEN Work Package 1 Analytic framework for design and planning of zero emission neighbourhoods (ZEN). The goal for WP 1 is to develop definitions, targets and benchmarking for ZEN, based on customized indicators and quantitative and qualitative data. Additionally, life cycle assessment methodology for energy and emissions at neighbourhood scale will be developed, as well as a citizen-centred architectural and urban toolbox for design and planning of ZEN.
A dynamic building stock model has been developed for energy- and GHG-emission scenario analyses of neighbourhoods. The model is generic and flexible and can be used to model any neighbourhood where building stock data is available. It makes use of a description of the current stock, as well as plans for construction, demolition and renovation activities in the neighbourhood. If plans are not available, the model may simulate stock activities by use of probability distributions. The neighbourhood building stock is segmented by use of archetypes defined by the buildings’ age, renovation state and floor area classes. Examples are grouping the two floor area types single family houses (SFH) and terraced houses (TH) together into a detached dwellings floor area class or grouping primary schools and secondary schools into a floor area class called “school buildings”. Hourly energy demand is estimated using delivered energy intensity profiles given for different archetypes of buildings or empirical data. Any number of different energy carriers and purposes can be defined and monthly or yearly carbon emission intensities can be given for each individual carrier. This serves as a basis to estimate hourly, monthly or yearly delivered energy and GHG emissions for a given neighbourhood under study.
Two cases are analysed in this report: i) a hypothetical case of an imaginary neighbourhood consisting of apartment block (AB) and SFH dwellings, and ii) the Gløshaugen campus of the Norwegian University of Science and Technology (NTNU). Gløshaugen campus is a neighbourhood that has a high complexity of floor area types and usage. The purpose of the two very different case studies is not to provide reliable case studies at present, but to demonstrate how the model is capable of long-term analyses of both homogenous and complex neighbourhoods in order to offer detailed understanding of possible future hourly energy use and GHG emissions.