An energy consumption model for the Algerian residential building’s stock, based on a triangular approach: Geographic Information System (GIS), regression analysis and hierarchical cluster analysis.
Residential energy consumption (REC); housing stock; energy modelling; Linear Regression (MLR); spatialisation; Clustering; Algeria
Abstract :
[en] Modelling residential energy consumption (REC) represents a key step towards the implementation of energy transition policies for more sustainable cities. Developing such policies requires considering the characteristics of the residential building stock (RBS). In the literature, REC modelling is generally applied on a single or set of cities- provinces, through limited approaches, using data from one typical year. In this paper, an energy consumption model for the entire Algerian RBS is developed through a triangular approach combining Geographic Information System, regression analysis and hierarchical clustering, applied to all provinces from 1995 to 2018. This allows mapping the spatial-temporal distribution of REC and RBS, developing a REC model, and dividing all provinces into clusters based on their REC behaviour. Provinces are aggregated into four clusters and four provinces are identified as archetypes. The results highlight that, besides the size of the RBS, REC is highly dependent on the electricity and gas connection rates. However, the influence of GDP and urban density only play a minor role. This can be explained by the evolving demands in thermal comfort associated with access to energy networks. The likely impact of increased gas
and electricity connection represents a crucial factor in the design of energy policies.
Nguyen, Bich Ngoc ; Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis)
Teller, Jacques ; Université de Liège - ULiège > Département ArGEnCo > Urbanisme et aménagement du territoire
Language :
English
Title :
An energy consumption model for the Algerian residential building’s stock, based on a triangular approach: Geographic Information System (GIS), regression analysis and hierarchical cluster analysis.
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