Evidence shows that people have a major impact on building performance. Occupants’ impact is especially important in social housing, where their occupants may present greater vulnerabilities, and their needs are not always considered. This study aims to analyse the socio-demographic influence in social rental housing concerning hygrothermal comfort and energy consumption in a case study located in Vitoria, Spain during the first 4-month period of 2020 and 2021 (during and after COVID-19 lockdown). An innovative data management system is included, where the users and administration can see in real-time the temperature and consumption in the dwellings. A 2-phase method has been applied; phase 1 is associated with outdoor climate conditions, building properties and social profile. Phase 2 determined the results in energy consumption, indoor hygrothermal comfort and occupant energy-use pattern. The results show that the comfort levels and energy consumption vary according to the analysed social profiles, as well as the heating activation periods and domestic hot water system usage. In conclusion, socio-demographic characteristics of social housing households influence the hygrothermal comfort of their dwellings, occupants’ behaviour and heating and domestic hot water energy consumption.
Indoor Air Quality perception in education centres has being a special concern based on their high occupancy and lack of ventilation. This study aims to present an overview of 20 relevant previous studies carried out in the last 5 years regarding monitoring and evaluating Indoor Air Quality in education centres. This analysis focuses on four specific aspects: general description of each study, ventilation typology, indicators measured and the number of locations measured. The results show that 60 % of the studies were located in an urban context, 75 % included primary schools and 75 % included naturally ventilated buildings. Indoor and outdoor was measured in 60 % of the studies. The most measured indicators were CO2 (60 %), PM2.5 (55 %), Temperature (50 %) and Relative Humidity (40 %) all indoor. In 60 % of studies were carried in more than five centres, mostly two rooms were measured and 35 % of studies placed one sensor per room. This can be a major limitation, as the monitored data may differ significantly from the actual situation. In conclusion, the greater correlations found relating to what indicators have been measured, were the ventilation typology and the location of the building, which influences what parameters and concentration can be expected.
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