Occupancy patterns are necessary to estimate energy demand and evaluate thermal comfort in households. Because of this, many European countries are developing representative domestic schedules to replace outdated criteria. This paper evaluates the state of knowledge of UK domestic occupancy patterns and develops new domestic occupancy profiles for England. The presented research (1) characterizes methods for collecting occupancy data and inferring patterns; (2) identifies and assesses the quality of categories of occupancy patterns used in building simulation; and (3) develops updated occupancy profiles. A systematic scoping review identified social and monitoring surveys as the most deployed data-collection methods. A systematic literature review also established that the occupancy categories most frequently used in UK building simulation are (a) a family with dependent children where the parents work full time; and (b) a retired elderly couple who spend most of their time indoors. The interview sample from the English Housing Survey 2014–15 was used to map household typologies. Results show that categories (a) and (b) combined amount to only 19% of England’s households, which suggest models are over-reliant on these groups. Considering this result, the paper develops occupancy patterns for England derived from 2015 UK Time Use Survey diaries for each household typology previously identified.

The UK aims to reduce its carbon emissions focusing on the renovation of its existing building stock, particularly on the residential sector. Within this group, social housing is an excellent candidate for retrofits, but faces specific challenges. The cases presented in this study are social housing tower blocks in the City of Portsmouth, which are destined to be retrofitted but present particularities within its architecture, heating system, and occupants’ behavior and comfort perception. This study presents an analysis of the thermal performance of these buildings after different retrofit intervention scenarios were undertaken to evaluate heating loads, comfort and overheating. A dynamic simulation model was developed using the software TRNSYS and included performance evaluation under future climate change scenarios. Firstly, the buildings were modelled under different occupier energy profiles and the results in all cases, show that the physical properties of the building lead to a high level of energy consumption and discomfort. Moreover, the simulation of possible retrofits demonstrated that improving the building envelope to meet 2010 Building Regulations or stricter standards would result in a decrease of more than 80% of the heating load but would result in overheating if no adequate shading is installed. The analysis indicates that the best performing retrofit corresponded to a mixed system of MHRV for heating and natural ventilation during the summer. The paper concludes that (a) existing buildings are inefficient and a retrofit would result in a complete change in their energy performance and (b) that a thorough economic appraisal is required to select the best environmentally and economically viable interventions.

Smart heating controls are being introduced in the domestic sector with the aim of reducing heating demand in buildings. However, the impact of controls on heat demand is not fully understood. This study set out to add empirical evidence to Kempton’s theory on mental models of home heat controls. With this purpose, radiator setpoint records from smart thermostatic valves in 47 flats from a care home in the South of England were evaluated over a 12-month period. Three types of households were identified: (i) low interactors who do not have interaction, or have minimal interaction, with the controls (24.5%); (ii) medium interactors who adjust their setpoint when the outdoor temperature changes and whose behavior is comparable to households that have a “feedback” mental model (49%); and (iii) high interactors who adjust the setpoint based on their own strategy, which does not necessarily follow outdoor temperature changes and reflects a lack of understanding of how the controls work (26.5%). These results highlight the contrast between expected and actual usage of home heat controls, as only half of the residents showed a behavior that is consistent with the principles of operation of the STVs.