The paper presents a spatially explicit and “bottom-up” methodology for the building stock analysis of the residential sector. It integrates different input data in a Geographical Information System (GIS), without using the “archetypes approach” and simulation tools. In particular, the energy balance at the building level (BL) for the whole Valle d'Aosta region (Italy) is addressed, using the Italian Ministerial Decree 26/06/2009 and the UNI/TS 11300-1:2014 standard. Main outputs are the estimation of the geo-referenced heating demand of the residential buildings for the case study area (almost 42,000 buildings), and the development of a methodology that can be applied at different scales. The application of the methodology to the case study slightly overestimates the total thermal demand of the residential building stock, especially referring to the more energy demanding buildings. However, being the method influenced by data availability, the quality is expected to improve with newly available data. The proposed GIS-based methodology is designed to be part of a broader Spatial Decision Support System (SDSS) for sustainable energy plans that integrate renewable sources in the building stock energy renovation. © 2019
A bottom-up spatially explicit methodology to estimate the space heating demand of the building stock at regional scale
Novelli, Antonio
;
2020-01-01
Abstract
The paper presents a spatially explicit and “bottom-up” methodology for the building stock analysis of the residential sector. It integrates different input data in a Geographical Information System (GIS), without using the “archetypes approach” and simulation tools. In particular, the energy balance at the building level (BL) for the whole Valle d'Aosta region (Italy) is addressed, using the Italian Ministerial Decree 26/06/2009 and the UNI/TS 11300-1:2014 standard. Main outputs are the estimation of the geo-referenced heating demand of the residential buildings for the case study area (almost 42,000 buildings), and the development of a methodology that can be applied at different scales. The application of the methodology to the case study slightly overestimates the total thermal demand of the residential building stock, especially referring to the more energy demanding buildings. However, being the method influenced by data availability, the quality is expected to improve with newly available data. The proposed GIS-based methodology is designed to be part of a broader Spatial Decision Support System (SDSS) for sustainable energy plans that integrate renewable sources in the building stock energy renovation. © 2019I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
