Passive Cooling Strategies for Multi-storey Residential Buildings in Tehran, Iran and Swansea, UK

Abstract

Like the UK, the residential sector in Iran has a significant share of the national energy consumption. Therefore, efforts are needed to reduce energy use and greenhouse gas (GHG) emissions from dwellings. This paper discusses two case studies for residential apartment buildings and explores the cooling strategies which could be adopted to reduce energy usage and the associated GHG emissions. For case study one (in Tehran) results from dynamic thermal modelling and simulation tests are presented that assess the effectiveness of a number of design cooling strategies. These include appropriate orientation, solar shading and thermal mass with night time ventilation. These strategies are seen as effective methods to control heat gain and to dissipate excess heat from the residential apartment building during the summer. For case study two (in Swansea) pilot results from spot tests undertaken during interviews with apartment occupants are presented. These spot results illustrate that dynamic thermal modelling should have been undertaken by the design team to inform the design decisions for this building, which was completed and occupied in November 2011, since internal conditions exceed recommended comfort conditions for level four (of the code for sustainable homes) dwellings. Furthermore, measures such as solar shading may need to be retrofitted and combined with a change to the ventilation strategy to reduce overheating during the year. The basis for the paper is to compare the results of two residential apartment buildings that both experience similar problems of overheating, even though they are located in two different countries and adopt different methodologies for recording the data. Lessons adopted as part of case study one to reduce overheating are being considered as potential solutions for case study two.KeywordsThermal MassIndoor TemperatureSpot MeasurementCool StrategyCooling Energy DemandThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.