Influencing factors in energy use of housing blocks:
A new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects
SPRINGER LINK | Energy Efficiency vol. 10 Issue 2 pag. 359-382 July 2016
a CIMNE-Cetre Internacional de Mètodes Numèrics en Enginyeria, Building Energy and Environment Group, Edific GAIA (TR14), Terrassa
b Computer Science, Universitat Politècnica de Catalunya (UPC Barcelona Tech), Campus Nord UPCBarcelona
c CIMNE-Cetre Internacional de Mètodes Numèrics en Enginyeria, Building Energy and Environment Group, Edific GAIA (TR14), Terrassa
d PROMETEO Researcher, Instituto Nacional de Eficiencia Energética y Energías Renovables (INER)QuitoEcuador
In recent years, big efforts have been dedicated to identify which are the factors with highest influence in the energy consumption of residential buildings. These factors include aspects such as weather dependence, user behaviour, socio-economic situation, type of the energy installations and typology of buildings. The high number of factors increases the complexity of analysis and leads to a lack of confidence in the results of the energy simulation analysis. This fact grows when we move one step up and perform global analysis of blocks of buildings.
The aim of this study is to report a new methodology for the assessment of the energy performance of large groups of buildings when considering the real use of energy. We combine two clustering methods, Generative Topographic Mapping and k-means, to obtain reference dwellings that can be considered as representative of the different energy patterns and energy systems of the neighbourhood. Then, simulation of energy demand and indoor temperature against the monitored comfort conditions in a short period is performed to obtain end use load disaggregation. This methodology was applied in a district at Terrassa City (Spain), and six reference dwellings were selected. Results showed that the method was able to identify the main patterns and provide occupants with feasible recommendations so that they can make required decisions at neighbourhood level. Moreover, given that the proposed method is based on the comparison with similar buildings, it could motivate building occupants to implement community improvement actions, as well as to modify their behaviour.