Parametric Study of different Levels of Detail in buildings for the estimation of Annual Heating Demand: A case study in London, UK

Abstract

In the 21st century, the importance of energy in developing countries is indisputable. In the whole wide world, the building stock is responsible for the two fifths of the total world annual energy consumption. Over recent years, the refurbishment of the existing building stock, with the purpose of being transmuted into energy efficient, and the construction of sustainable and low energy buildings, has interested the broader construction sector. Taking into account the predictions about the future climate, the need for the expeditious refurbishment of entire building blocks is essential. The Level of Detail (LoD), that it is the method used to display a project's construction details, is an important factor to consider while modelling energy at the urban scale. A parametric study regarding the data requirements for the estimation of the annual residential heat demand in city of London has been conducted for this research project. More particularly, the requirement of the observation of the actual roof type (LoD2) and the window to wall ratio (LoD3) has been examined in two different areas. The results have shown that there is a minor difference from the upgrade of lower to higher LoD, regarding these parameters. This means that the time and money – consuming procedure of observation for the roof types and calculation of windows to wall ratio of buildings at an area is not necessary, and energy performance of buildings could be estimated with an assumption from archetypes and building ages. Finally, in future work, from the energy aspect, the refurbishment date, as well as different air change rate of buildings or indoor temperatures, could be taken into consideration for more representative results, at mixed age areas, but also, from the data requirement and modelling point of view, studies could be conducted regarding the simplest way to link the required data from different surveying companies into a single dataset that could be used with ease from analyzers and by this way help policy makers, regarding the reduction of the residential energy demand and carbon dioxide emissions