• Energy Research

AbstractDomestic 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.

Abstract In this paper, panel methods are applied in new and innovative ways to predict daily mean internal temperature demand across a heterogeneous domestic building stock over time. This research not only exploits a rich new dataset but presents new methodological insights and offers important linkages for connecting bottom-up building stock models to human behaviour. It represents the first time a panel model has been used to estimate the dynamics of internal temperature demand from the natural daily fluctuations of external temperature combined with important behavioural, socio-demographic and building efficiency variables. The model is able to predict internal temperatures across a heterogeneous building stock to within ∼0.71 °C at 95% confidence and explain 45% of the variance of internal temperature between dwellings. The model confirms hypothesis from sociology and psychology that habitual behaviours are important drivers of home energy consumption. In addition, the model offers the possibility to quantify take-back (direct rebound effect) owing to increased internal temperatures from the installation of energy efficiency measures. The presence of thermostats or thermostatic radiator valves (TRVs) are shown to reduce average internal temperatures, however, the use of an automatic timer is shown to be statistically insignificant. The number of occupants, household income and occupant age are all important factors that explain a quantifiable increase in internal temperature demand. Households with children or retired occupants are shown to have higher average internal temperatures than households who do not. As expected, building typology, building age, roof insulation thickness, wall U -value and the proportion of double glazing all have positive and statistically significant effects on daily mean internal temperature. In summary, the model can be either used to make statistical inferences about the importance of different factors for explaining internal temperatures or as a predictive tool. However, a key contribution of this research is the possibility to use this model to calibrate existing building stock for behaviour and socio-demographic effects leading to improved estimations of domestic energy demand.

This analysis compares the difference between the Energy Performance Certificate (EPC)-modelled and smart-meter measured annual energy use on a like-for-like basis in 1,374 gas-heated British households from the Smart Energy Research Lab (SERL) Observatory. EPCs and metered energy use were converted to primary energy use intensity (PEUI) to provide a comparison of the same quantity for the first time.We show that EPCs predict significantly more energy use than metered in homes in Great Britain. EPC bands A and B show no statistically significant difference, but all other bands show a significant gap which increases as EPC rating worsens. The PEUI gap widens from -26 kWh/yr/m2 (-8%) for band C to -276 kWh/y/m2 (-48%) for bands F and G. Unlike previous research, we show that the difference persists in homes matching the EPC-model assumptions regarding occupancy, thermostat set-point and whole-home heating; suggesting that occupant behaviour is unlikely to fully explain the discrepancy. EPCs are a core tool in the residential energy sector, and the gap between EPC-modelled and metered energy use could have a significant impact on policy, research, and industry. Future research should investigate disaggregated components of energy use, the underlying thermal model, and assumptions regarding building characteristics.

AbstractWe tested whether the colour temperature of the illumination (realised through manipulating the ceiling light) impacted on thermal comfort, based on the hypothesis that a lower colour temperature is associated with feeling warmer and a higher colour temperature with feeling cooler. If confirmed, then light might be a tool for energy-saving through allowing ambient air temperatures to vary over a wider range and hence reducing the need for space heating and cooling.Testing took place in a climate chamber. In Study 1, comfort ratings were collected using thermal comfort surveys (N=32). In Study 2, an observational design was used, where changes in clothing level, interpreted as thermal discomfort responses, were observed (N=32). We compared comfort ratings and changes in clothing level under light with a colour temperature of 2700K vs. 6500K. Results partly confirmed the hypotheses: both self-report and observation indicated higher comfort under the low colour temperature. Further research will need to replicate findings in a real-world setting to see if light might indeed be a tool to modulate thermal comfort, and hence reduce usage of heating and cooling.

AbstractObjectiveThis study examines the extent that temperatures in English living rooms correspond to standard assumptions made in established UK building stock models.MethodsSpot temperature measurements taken every 45min over 92 winter days in 248 homes in England were analyzed and compared to the assumed thermostat setting of 21°C inside and outside the assumed heating periods.ResultsHomes on average displayed lower internal temperatures during assumed heating periods and significantly shorter durations of heating to 21°C than common models assume, with about 20% of homes never reaching the assumed demand temperature of 21°C. Data showed a difference of only about 45min in the duration of temperatures at or above the demand temperature for weekdays and weekends, contrary to the assumed difference of 7h. Variability between homes was large.ConclusionThese findings suggest that currently used standard assumptions of heating demand and heating duration do not accurately reflect the living room temperatures of dwellings in England.Practice implicationsStandard assumptions might have to be revised, in particular regarding the weekday–weekend differentiation. The prediction of internal temperature for a given home contains potential large error when using standard assumptions.

Abstract The variability of renewable energy is widely recognised as a challenge for integrating high levels of renewable generation into electricity systems. However, to explore its implications effectively, variability itself should first be clearly understood. This is particularly true for national electricity systems with high planned penetrations of renewables and limited interconnection such as the UK. Variability cannot be considered as a distinct resource property with a single measurable parameter, but is a multi-faceted concept best described by a range of distinct characteristics. This paper identifies relevant characteristics of variability, and considers their implications for energy research. This is done through analysis of wind, solar and tidal current resources, with a primary focus on the Bristol Channel region in the UK. The relationship with electricity demand is considered, alongside the potential benefits of resource diversity. Analysis is presented in terms of persistence, distribution, frequency and correlation between supply and demand. Marked differences are seen between the behaviours of the individual resources, and these give rise to a range of different implications for system integration. Wind shows strong persistence and a useful seasonal pattern, but also a high spread in energy levels at time scales beyond one or two days. The solar resource is most closely correlated with electricity demand, but is undermined by night-time zero values and an even greater spread of monthly energy delivered than wind. In contrast, the tidal resource exhibits very low persistence, but also much greater consistency in energy values assessed across monthly time scales. Whilst this paper focuses primarily on the behaviour of resources, it is noted that discrete variability characteristics can be related to different system impacts. Persistence and predictability are relevant for system balancing, whereas statistical distribution is more relevant when exploring issues of asset utilisation and energy curtailment. Areas of further research are also identified, including the need to assess the value of predictability in relation to other characteristics.

The most commonly used family of models in representing the UK building stock, BREDEM (the Building Research Establishment Domestic Energy Models), assume that all homes exhibit the same heating pattern and hence can be expected to have similar temperature profiles over the course of a day. The presented research shows that homes differ significantly in their respective temperature profile over the course of the day. A cluster analysis performed on temperature data from 275 living rooms in English homes over three winter months resulted in four different clusters of temperature profiles. The clusters differ significantly in their shape, as revealed by visual inspection, and supported by significant differences in minimum and maximum temperatures and temperature variability across the day. About 40% of homes showed a bimodal temperature pattern as assumed under BREDEM. However, the remaining 60% showed very different profiles. These findings challenge the assumption that one standard pattern fits all homes...