A methodology for predicting the energy performance and indoor thermal comfort of residential stocks on the neighbourhood and city scales. A case study in Spain
2016
Braulio-Gonzalo, Marta | Bovea, María Dolores | Ruá, María José | Juan, Pablo
The aim of the study is to present a developed bottom-up-based methodology for predicting the energy performance of residential stocks on the neighbourhood and city scales. This methodology enables predicting the energy demand and discomfort hours (heating and cooling) taking into account urban and building factors such as urban block type, street height-width ratio and solar orientation of the main façade, and shape factor and year of construction of the building, respectively. For this purpose, a four-staged methodology consisting in (1) urban taxonomy characterisation, (2) energy performance assessment, (3) statistical modelling and (4) stock aggregation is proposed, which combines building physical modelling and statistical inference in a Geographical Information System environment to provide an intuitive visual interface that represents final results on urban energy maps. The methodology was implemented in a medium-sized Spanish Mediterranean city as a case study, which allowed estimating the passive energy performance of a neighbourhood and setting building and urban design strategies. Results allowed concluding that the intrinsic parameters of the urban morphology play an important role on passive energy performance and important energy demand savings can be achieved when considering morphological urban aspects in new planning developments. This methodology is an efficient tool that can help stakeholders and local authorities in decision-making processes that focus both on developments of new urban areas taking into account energy requirements and on identifying and prioritising existing residential stocks in need of rehabilitation in energy terms.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par National Agricultural Library
Découvrez la collection de ce fournisseur de données dans AGRIS