Indoor temperatures in UK dwellings: investigating heating practices using field survey data

In 2010 the housing stock was responsible for 30.5% of all energy consumed in the UK. The UK government has set a transition target to reduce the energy used from space heating in dwellings by 29% by 2020 as part of their drive to lower CO2 emissions and mitigate the risks of global climate change. Housing stock energy models have been developed as research tools to identify pathways to a low energy future. These tools use assumptions about how homes are heated that may reduce their effectiveness at making accurate energy predictions. This thesis describes the collection and analysis of temperature data from over 300 homes in Leicester to develop better understanding of how dwellings are heated. The temperature measurements were assessed for error and a final sample of 249 dwellings was established. Mean winter temperatures (December February) were found to be 18.5°C and 17.4°C for living rooms and bedrooms which are comparable with temperatures reported in previous studies. Statistically significant relationships were established between seven descriptors; three technical (house type, house age and wall type) and four social (household size, employment status, age of oldest occupants and tenure). Only 24% of the variation in mean winter temperature could be explained by these descriptors. Ten heating practice metrics were developed to give insight into how homes are heated; these included the duration of the heating period and the average temperature when heated. Statistically significant relationships were found between the heating practices and a number of technical and social household descriptors. It is concluded that the variation in heating practices which relates to social household descriptors will result in models being unable to make accurate predictions at the regional of city scale. Furthermore, this work has shown flaws in the idealised temperature profile as used in BREDEM. It is suggested that the findings of this work are considered in the development of future stock models

Summertime temperatures and thermal comfort in UK homes

Internal summertime temperatures measured in 268 homes in the UK city of Leicester are reported. The hourly data was collected from living rooms and bedrooms during the summer of 2009, which was generally cool but with a short hot spell. Some household interviews were conducted. The sample of homes is statistically representative of the socio-technical characteristics of the city’s housing stock. The data provides insight into the influence of house construction, energy system usage and occupant characteristics on the incidence of elevated temperatures and thermal discomfort. The warmest homes were amongst the 13% that were heated. Significantly more of these were occupied by those over 70 who are particularly vulnerable to high temperatures. The national heatwave plan might usefully caution against summertime heating. Temperatures in the 230 free-running homes were analysed using both static criteria and criteria associated with the BSEN15251 adaptive thermal comfort model. These indicated that that flats tended to be significantly warmer than other house types. Solid wall homes and detached houses tended to be significantly cooler. It is argued that adaptive criteria provide a valuable and credible framework for assessing internal temperatures in free-running UK homes. However, the temperatures in the Leicester homes were much lower than anticipated by the BSEN15251 model. Numerous possible reasons for this discrepancy are discussed

Summertime temperatures in 282 UK homes: thermal comfort and overheating risk

Summertime temperatures in UK homes are a matter of increasing concern, particularly because of global warming and an increased incidence of heat waves. Refurbishment adds to uncertainty about the resilience of UK homes to climate change. This paper examines internal summertime temperatures in the living and bedrooms of 282 homes in the UK city of Leicester. This is a statistically representative sample of the citys housing stock. The generally cool monitoring period included a short period of hot weather. Occupant behaviour had a significant impact on internal temperature, 13% of the homes were actively heated even during the spell of hot weather. In the 230 unheated homes, 28% of the living rooms and 88% of bedrooms were classed as severely overheated, as judged by the static, CIBSE, criteria. In contrast, 64% of the living rooms and 71% of the bedrooms were judged uncomfortably cool as defined by the BSEN15251 Cat II adaptive thermal comfort standard

Smart homes, control and energy management:How do smart home technologies influence control over energy use and domestic life?

By introducing new ways of automatically and remotely controlling domestic environments smart technologies have the potential to significantly improve domestic energy management. It is argued that they will simplify users’ lives by allowing them to delegate aspects of decision-making and control - relating to energy management, security, leisure and entertainment etc. - to automated smart home systems. Whilst such technologically-optimistic visions are seductive to many, less research attention has so far been paid to how users interact with and make use of the advanced control functionality that smart homes provide within already complex everyday lives. What literature there is on domestic technology use and control, shows that control is a complex and contested concept. Far from merely controlling appliances, householders are also concerned about a wide range of broader understandings of control relating, for example, to control over security, independence, hectic schedules and even over other household members such as through parenting or care relationships. This paper draws on new quantitative and qualitative data from 4 homes involved in a smart home field trial that have been equipped with smart home systems that provide advanced control functionality over appliances and space heating. Quantitative data examines how householders have used the systems both to try and improve their energy efficiency but also for purposes such as enhanced security or scheduling appliances to align with lifestyles. Qualitative data (from in-depth interviews) explores how smart technologies have impacted upon, and were impacted by, broader understandings of control within the home. The paper concludes by proposing an analytical framework for future research on control in the smart home

Tagore's School and Methodology: Classrooms Without Walls

This paper argues that Rabindranath Tagore, a very practical man, developed a distinctive and successful educational methodology over the course of his work in educational systems. The paper seeks to show that Tagore drew inspiration and direction from extraordinary times, and extraordinary people of those times. The paper establishes the Tagore family’s place within the ongoing Bengali Renaissance; and to Tagore’s place among remarkable individuals, particularly Jagadish Chandra Bose and Patrick Geddes. The paper looks to the emergence of the poet’s educational institutions from spiritual and technological viewpoints. An attempt is made to show that Tagore’s educational establishments were methodologically developed, can claim to be part of his poetic legacy; and that telepresence technologies of the twenty-first century might offer good service to those establishments as they continue to evolve

Identifying the time profile of everyday activities in the home using smart meter data

Activities are a descriptive term for the common ways households spend their time. Examples include cooking, doing laundry, or socialising. Smart meter data can be used to generate time profiles of activities that are meaningful to households’ own lived experience. Activities are therefore a lens through which energy feedback to households can be made salient and understandable. This paper demonstrates a multi-step methodology for inferring hourly time profiles of ten household activities using smart meter data, supplemented by individual appliance plug monitors and environmental sensors. First, household interviews, video ethnography, and technology surveys are used to identify appliances and devices in the home, and their roles in specific activities. Second, ‘ontologies’ are developed to map out the relationships between activities and technologies in the home. One or more technologies may indicate the occurrence of certain activities. Third, data from smart meters, plug monitors and sensor data are collected. Smart meter data measuring aggregate electricity use are disaggregated and processed together with the plug monitor and sensor data to identify when and for how long different activities are occurring. Sensor data are particularly useful for activities that are not always associated with an energy-using device. Fourth, the ontologies are applied to the disaggregated data to make inferences on hourly time profiles of ten everyday activities. These include washing, doing laundry, watching TV (reliably inferred), and cleaning, socialising, working (inferred with uncertainties). Fifth, activity time diaries and structured interviews are used to validate both the ontologies and the inferred activity time profiles. Two case study homes are used to illustrate the methodology using data collected as part of a UK trial of smart home technologies. The methodology is demonstrated to produce reliable time profiles of a range of domestic activities that are meaningful to households. The methodology also emphasises the value of integrating coded interview and video ethnography data into both the development of the activity inference process

Measured internal temperatures in UK homes: a time series analysis and modelling approach

This paper presents an analysis of internal air temperatures measured hourly in the living rooms of 230 domestic buildings in the city of Leicester, UK. Time series analysis is used to identify the mechanisms that shape room temperatures, during the summertime period of July and August, in rooms that are neither mechanically heated nor cooled, and to develop empirical models of room temperatures for use in predicting future temperatures based on past measured values and on future weather conditions. Such models can enable overheating risk alerts for homeowners and public authorities to be more accurately estimated and targeted

Development of a statistical model for the prediction of overheating in UK homes using descriptive time series analysis

Overheating risk in dwellings is often predicted using modelling techniques based on assumptions of heat gains, heat losses and heat storage. However, a simpler method is to use empirical data to predict internal temperatures in dwellings based on external climate data. The aim of this research is to use classical time series descriptive analysis and construct statistical models that allow the prediction of future internal temperatures based external weather data. Initial results from the analysis of a living room in a house show that the proposed method can successfully predict the risk of overheating based on four different overheating criteria

Measured internal temperatures in UK homes: a time series analysis and modelling approach

This paper presents an analysis of internal air temperatures measured hourly in the living rooms of 10 domestic buildings in the city of Leicester, UK. Time series analysis is used to develop empirical models of room temperatures in rooms that are neither mechanically heated nor cooled, during the summertime period of July and August 2009. The models are used in predicting future temperatures based on past measured values. Such models can enable overheating risk alerts for homeowners and public authorities to be more accurately estimated and targeted

Multicentre analysis of incidental findings on low-resolution CT attenuation correction images : an extended study

Objective: To review new incidental findings detected on low-resolution CT attenuation correction (CTAC) images acquired during single-photon emission CT-CT myocardial perfusion imaging as an extension to our initial study. Methods: CTAC images acquired as part of myocardial perfusion imaging performed using single-photon emission CT at four UK nuclear medicine centres were evaluated as part of a multicentre study. New incidental findings that were considered to be clinically significant were evaluated further. Positive-predictive value (PPV) was determined at the time of definitive diagnosis. Results: Out of 3485 patients, 962 (28%) patients had a positive finding on the CTAC image, of which 824 (24%) were new findings. 84 (2.4%) patients had findings that were considered clinically significant at the time of the CTAC report and which had not been previously diagnosed. However, only 10 (0.29%) of these had findings that were confirmed as clinically significant, with the potential to be detrimental to patient outcome, after follow-up and definitive diagnosis. Conclusion: The overall PPV from all centres over the 2-year period was 12%. Each centre achieved what we considered to be low PPVs with no significant difference between the present and initial studies. The additional data from the combined studies show that, statistically, there is no significant difference between the PPVs from any of the centres. We conclude that routine reporting of CTAC images is not beneficial. Advances in knowledge: This study combined with the previous study offers a unique evaluation of new clinically significant incidental findings on low-resolution CT images in an attempt to determine the benefit of reporting the CTAC images