Comparative analysis of energy demand and CO2 emissions on different typologies of residential buildings in Europe

Abstract

The building sector accounts for one third of the global energy consumption and it is expected to grow in the next decades. This evidence leads researchers, engineers and architects to develop innovative technologies based on renewable energies and to enhance the thermal performance of building envelopes. In this context, the potential applicability and further energy performance analysis of these technologies when implemented into different building typologies and climate conditions are not easily comparable. Although massive information is available in data sources, the lack of standardized methods for data gathering and the non-public availability makes the comparative analyses more difficult. These facts limit the benchmarking of different building energy demand parameters such as space heating, cooling, air conditioning, domestic hot water, lighting and electric appliances. Therefore, the first objective of this study consists in providing a review about the common typologies of residential buildings in Europe from the main data sources. This study contains specific details on their architecture, building envelope, floor space and insulation properties. The second objective consists in performing a cross-country comparison in terms of energy demand for the applications with higher energy requirements in the residential building sector (heating and domestic hot water), as well as their related CO2 emissions. The approach of this comparative analysis is based on the residential building typology developed in TABULA/EPISCOPE projects. This comparative study provides a reference scenario in terms of energy demand and CO2 emissions for residential buildings and allows to evaluate the potential implementation of new supply energy technologies in hot, temperate and cold climate regions. From this study it was also concluded that there is a necessity of a free access database which could gather and classify reliable energy data in buildings.