Domestic demand-side response (DSR), if widely adopted, could help make electricity more secure, clean and affordable. However, little is known about consumer demand for different approaches to achieving DSR. This study aimed to gauge the acceptability of a range of DSR tariffs, and explore factors affecting it. An online between-subjects survey experiment was deployed to a representative sample of bill payers in Great Britain (N=2002), testing five tariffs including static/dynamic time of use (TOU) pricing (with/without automated response to price changes) and direct load control (DLC) of heating on a below-average flat rate.The tariff permitting limited DLC of heating was significantly (p<.01) more popular than the TOU tariffs. This was surprising given evidence for concern around loss of control in DLC, and suggests that for many people DLC is acceptable in principle (within tight bounds and with override ability). The option of automated response made dynamic TOU (otherwise the least popular tariff) as acceptable as static TOU. This is important because dynamic TOU offers additional network benefits, while automation can improve duration and reliability of response. The TOU tariffs were rated highly for giving people control over spending on electricity, but other factors were more associated with overall acceptance

Evaluating how much heat is lost through external walls is a key requirement for building energy simulators and is necessary for quality assurance and successful decision making in policy making and building design, construction and refurbishment. Heat loss can be estimated using the temperature differences between the inside and outside air and an estimate of the thermal transmittance (U-value) of the wall. Unfortunately the actual U-value may be different from those values obtained using assumptions about the materials, their properties and the structure of the wall after a cursory visual inspection.In-situ monitoring using thermometers and heat flux plates enables more accurate characterisation of the thermal properties of walls in their context. However, standard practices require that the measurements are carried out in winter over a two-week period to significantly reduce the dynamic effects of the wall's thermal mass from the data.A novel combination of a lumped thermal mass model, together with Bayesian statistical analysis is presented to derive estimates of the U-value and effective thermal mass. The method needs only a few days of measurements, provides an estimate of the effective thermal mass and could potentially be used in summer

The estimation of the thermophysical characteristics of building elements based on in situ monitoring enables their performance to be assessed for quality assurance and successful decision making in policy making, building design, construction and refurbishment. Two physically-informed lumped thermal mass models, together with Bayesian statistical analysis of temperature and heat flow measurements, are presented to derive estimates of the thermophysical properties of a wall. The development of a two thermal mass, three thermal resistance model (2TM) enabled the thermal structure of the wall to be investigated and related to the known physical structure of two heavy-weight walls of different construction: a solid brick wall and an aerated clay, plaster, woodfibre insulation and gypsum fibreboard wall. The 2TM model produced good match to the measured heat flux at both interior and exterior surfaces for both walls, unlike a one thermal mass model (1TM); Bayesian model comparison strongly supported the 2TM over the 1TM model to accurately describe the observed data. Characterisation of the thermal structure and performance of building elements prior to decision making in interventions will support the development of tailored solutions to maximise thermal comfort and minimise energy use through insulation, heating and cooling strategies

The call for action to transform the built environment and address the threats of climate change has been clearly made. However, to support the development, implementation and on-going evaluation of energy demand policy, a strong evidence base is needed to identify associations and establish underlying causes behind outcomes and variations in end-use energy demand within the population. A new approach to end-use energy demand research is presented which is founded on the interdisciplinary health sciences research framework of epidemiology, along with the establishment of a research centre. A case is made that through an ‘energy epidemiology’ approach a strong, population-level, empirically based research foundation can be advanced. Energy epidemiology is a whole-system approach that focuses on empirical research and provides a methodological framework for building physicists, engineers, sociologists and economists to engage in interdisciplinary work. The adaptation of the epidemiological approach to end-use energy demand studies will provide the means to observe and describe the trends and patterns of energy demand, to undertake and contextualize interventional studies, and to establish strong associations between factors that lead to an energy demand-related outcome or event. Such an approach would strengthen the evidence base to inform policy decisions and evaluate past intervention programmes or regulatory actions. Il a été clairement lancé un appel pour mettre en place des mesures propres à transformer le cadre bâti et à faire face aux menaces du changement climatique. Néanmoins, pour soutenir l'élaboration, la mise en œuvre et l'évaluation en continu de la politique relative à la demande énergétique, il est nécessaire de disposer d'une solide base de données factuelle pour identifier les associations et établir les causes sous-jacentes des résultats et des variations de la demande énergétique des utilisateurs finals au sein de la population. Il est présenté une nouvelle approche pour l'étude de la demande énergétique des utilisateurs finals, qui est fondée sur le cadre interdisciplinaire de l'épidémiologie utilisé pour les recherches dans les sciences de la santé, ainsi que sur l'établissement d'un centre de recherche. Il est fait valoir que, par une approche basée sur une « épidémiologie de l'énergie », il est possible de faire progresser la fondation d'une recherche empirique solide au niveau de la population. L'épidémiologie de l'énergie est une approche systémique globale qui est axée sur la recherche empirique et fournit un cadre méthodologique aux physiciens, ingénieurs, sociologues et économistes du bâtiment afin qu'ils participent à des travaux interdisciplinaires. L'adaptation de l'approche épidémiologique aux études relatives à la demande énergétique des utilisateurs finals donnera les moyens d'observer et de décrire les tendances et les schémas de la demande énergétique, d'entreprendre et de contextualiser des études interventionnelles, et d'établir les associations fortes entre les facteurs qui conduisent à un résultat ou à un événement lié à la demande énergétique. Une telle approche renforcerait la base de données factuelle afin d'éclairer les décisions en matière de politique et d'évaluer les programmes d'intervention passés ou les mesures réglementaires prises. Mots clés: bâtiments, cadre bâti, demande énergétique, épidémiologie, base factuelle, interdisciplinaire, politique publique, recherche

Domestic Energy Performance Certificates (EPCs) are used in the UK to provide energy efficiency ratings for use in policy and investment decisions on individual dwellings and at a stock level. There is evidence that the process of creating an EPC introduces measurement error such that repeat assessments of the same property give different ratings, compromising their reliability. This study presents a novel error analysis to estimate the size of this effect, using repeated EPC assessments of 1.6 million existing dwellings in England and Wales. A statistical model of how measurement error contributes to variation between repeated measurements is set out, and exploratory data analysis is used to decide how to apply this model to the available data. The results predict that the one standard deviation measurement error decreases with EPC rating, from around ± 8.0 EPC points on a rating of 35 to ±2.4 on a rating of 85. This predicted error is higher than the limit recommended in UK guidance except in very efficient buildings; it can also result in dwellings being rated in the wrong EPC band, for example it was estimated that 24% of band D homes are rated as band C.