Decarbonisation potential of multi-family Swedish Homes
Abstract: Global warming is mainly caused by greenhouse gas emissions. The building sector is responsible for a significant share of energy use and greenhouse gas emissions. Most of the existing buildings in Europe were built before 2001 and the vast majority will remain in place after 2050 when EU aims to achieve climate neutrality. Thus, renovation of the European building stock is needed to reduce operational energy and, therefore, emissions. In order to decide upon an energy renovation, one needs first to model the existing building, typically using energy simulations. Urban energy modeling for large building stocks is necessary in order to increase the rate of renovation; this requires large sets of data. However, personal inspection of the whole building stock is not realistic; on the other hand, large-scale open-source databases lack the required level of detail. This thesis project investigates a methodology for semi-automatically generating building energy models at urban scale. The energy models can be created based on open-access databases: OpenStreetMap, BETSI database, and Energy Performance Certifications database. OpenStreetMap is an open-access map database, and it contains building footprints. BETSI database is based on building statistics for Sweden, and it includes detailed construction information and thermal properties. Energy Performance Certifications database contains general construction information for specific buildings such as heated floor area, floor numbers, and energy performance of buildings. The developed methodology can derive the building footprint data from the OpenStreetMap database. 3D building models are then created with geometry data from Energy Performance Certificates and BETSI databases. Thermal properties can be determined from the BETSI database to create building energy models. The global warming potential of building operational energy is later calculated by the climate impacts of heating and electricity use. This methodology was illustrated in several case-studies building blocks from different geographical locations in Sweden and construction periods. Results on the case studies show that it is possible to semi-automatically generate building energy models that predict the energy performance without any input data from a user. The accuracy compared to measurements of space heating from the energy performance certificates was between 3% and 21%. However, more data is required to calibrate the building energy model for higher accuracy. This can be done by, for example, adjusting the simulation input data to fit the actual monthly energy use or by other input data from the user, such as the window-to-wall ratio.
AT THIS PAGE YOU CAN DOWNLOAD THE WHOLE ESSAY. (follow the link to the next page)