There is a increasing interest in modelling stock-level (i.e. local authority, re- gional or national) energy flows in buildings (both domestic and non-domestic) primarily as a means for technological and economic assessment of carbon abate- ment options. Modelling stock level building energy flows is a complex endeav- our that requires the bringing together of a range of different data-sets (cli- mate data, physical building data, occupant profiles, system profiles etc.) each with its own particular data-structure. Typically, this process can be time- consuming, repetitive and difficult to update. As new data is continually being produced, models can quickly become out-dated. We propose a semantically annotated database via an over-arching ontology that radically simplifies this process providing powerful new techniques to combine data-sets and query them. We demonstrate this technique through building up a full time-series of English Housing Survey (EHS) data (from 1970 onwards) which are not directly com- patible due to changes in survey methodologies over time. We then use the combined data-set to build up a picture of changing SAP levels for new build- ings over this period and plot them against mandated changes to the building regulations. The key demonstration here is the speed and the efficiency of the process rather than the data itself.
|Number of pages||28|
|Publication status||Unpublished - 2014|
- semantic annotations
- Time series analysis
- building energy modelling