Search

Highlights • The first stochastic models of manual overrides were developed from measured data. • Measurements were collected in ten UK dwellings and analysed over a heating season. • The drivers of manual heating override behaviour are investigated. • The models can be implemented in building performance simulation applications. Abstract This paper investigates the physical environmental (indoor and outdoor temperature and relative humidity, wind speed, solar radiation and rainfall) and contextual drivers (time of day) affecting occupants' manual space heating override behaviour during the heating season based on measurements collected in ten UK dwellings. Logistic regression modelling is used to understand the probability of occupants manually overriding their scheduled heating periods. To the authors' knowledge, these are the first stochastic models of manual heating override behaviour developed for residential buildings. The work reported in this paper suggests that occupants' manual overrides are influenced by indoor and outdoor temperature, indoor relative humidity, and solar radiation. In addition, the effects of the physical environmental variables varied in relation to the time of day. At night, none of the physical environmental variables influenced manual overrides. In the morning, afternoon and evening, manual overrides were governed by a mix of indoor air temperature, indoor relative humidity and solar radiation. The models presented can be used in building performance simulation applications to improve the inputs for space heating behaviour in residential buildings and thus the predictions of energy use and indoor environmental conditions. [ABSTRACT FROM AUTHOR]

This paper provides an analysis of the appliance ownership and use factors contributing to high electrical energy demand in UK homes. The data were collected during a large-scale, city-wide survey, carried out in Leicester, UK, in 2009–2010. Annual electricity consumption and appliance ownership and use were established for 183 dwellings and an odds ratio analysis used to identify the factors that led to high electricity consumption. Many of the appliance ownership and use factors have not previously been studied for the UK domestic sector. The results of this study should be of key interest to government policy makers and energy supply companies interested in the underlying drivers of the highly positively skewed distribution of UK domestic electricity use. The study identifies those appliances that could be targeted for technical improvements or subjected to campaigns to encourage more energy efficient use in order to reduce electricity consumption among high demand households. This paper builds on earlier work by the current authors which identified the households (socio-demographic and dwelling characteristics) most likely to be high electricity consumers. The current work provides the basis for advice and guidance to those households that would enable them to, over time, reduce their electricity use. [ABSTRACT FROM AUTHOR]

Highlights • A method is presented for non-intrusively inferring a building Heat Loss Coefficient using smart meter data. • For the sample data from the UK Energy Demand Research Project, a median Heat Loss Coefficient of 0.14 kW/°C and uncertainty of 15% was estimated. • The method was demonstrated to be reliably scalable to large numbers of buildings, enabling rapid thermal performance evaluation at a regional or national scale. • The method was demonstrated to produce stable results across several years, indicating its robustness to climate variation and occupant effects. Abstract Dwellings in the UK account for about 25% of global energy demand, of which 60% is space heating making this a key area for efficiency improvement. Dwelling UK Energy Performance Certificates (EPC) are currently based on surveyed data, rather than energy use monitoring. The installation of smart meters provides an opportunity to develop an EPC based on in situ dwelling thermal performance. This paper presents 'Deconstruct' – a method of estimating the as-built Heat Power Loss Coefficient (HPLC) of occupied dwellings as a measure of thermal performance, using just smart-meter and meteorological data. Deconstruct is a steady-state grey box building model combined with a data processing pipeline and a model fitting method that limits the effects of confounding factors. Smart meter data from 780 UK dwellings from the UK Energy Demand Research Project (EDRP), was used to calculate a median HPLC of 0.28 kW/°C (±15%). The stability of the estimate across multiple years of data with different weather and energy use was demonstrated. Deconstruct was found to be suitable for large scale inference of dwelling thermal properties using the UK's new smart metering data infrastructure. [ABSTRACT FROM AUTHOR]

This paper presents the methodological approach and findings of a simulation study of advanced energy conservation, generation and management technologies applied to two case study dwellings in the UK, so as to achieve net zero energy (NZE) target that includes a reduction of net regulated energy (HVAC) to 0 kWh/m 2 per year and energy generation of at least 50 kWh/m 2 per year. The performance of the dwellings are also tested for a change in energy use and risk of summertime overheating in future climates in the short (2030s), medium (2050s) and long term (2080s). The findings indicate that to meet the NZE targets, regulated loads need to reduce by about half (over current Building Regulations) with community (rather than building-level) renewables playing a major role. It is also found that the NZE targets, given current regulations, are not particularly difficult to achieve in design with regard to energy efficiency but are highly expectant with regard to renewable energy. Further, summertime overheating is projected to be a risk for net zero energy dwellings by the 2050s. Despite this the regulated energy use of both dwellings is projected to reduce by 11 kWh/m 2 /yr, accompanied by a small increase in renewable generation (2 kWh/m 2 /yr). [ABSTRACT FROM AUTHOR]

The UK housing stock is responsible for some 27% of national energy demand and associated carbon dioxide emissions. 80% of this energy demand is due to heating (60%) and domestic hot water (20%), the former reflecting the poor average thermal integrity of the envelope of the homes comprising this stock. To support the formulation of policies and strategies to decarbonise the UK housing stock, a large number of increasingly sophisticated Housing Stock Energy Models (HSEMs) have been developed throughout the past 25 years. After describing the sources of data and the spatio-temporal granularity with which these data are available to represent this stock, as well as the physical and social phenomena that are modelled and the range of strategies employed to do so, this paper evaluates the 29 HSEMs that have been developed and deployed in the UK. In this we consider the models’ predictive accuracy, predictive sensitivity to design parameters, versatility, computational efficiency, the reproducibility of predictions and software usability as well as the models’ transparency (how open they are) and modularity. We also discuss their comprehensiveness. From this evaluation, we conclude that current HSEMs are lacking in transparency and modularity, they are limited in their scope and employ simplistic models that limit their utility; in particular, relating to the modelling of heat flow and in the modelling of household behaviours relating to investment decisions and energy using practices. There is a need for an open-source and modular dynamic housing stock energy modelling platform that addresses current limitations, can be readily updated as new (e.g. housing survey) calibration data is released and be readily extended by the modelling community at large: improving upon the utilisation of scarce developmental resources. This would represent a considerable step forward in the formulation of housing stock decarbonisation policy that is informed by sound evidence. [ABSTRACT FROM AUTHOR]

This paper provides an analysis of the socio-economic and dwelling factors contributing to high electrical energy demand in UK domestic buildings. The socio-economic, dwelling and electricity consumption data were collected during a large-scale, city-wide survey, carried out in Leicester, UK, in 2009–2010. Annual electrical energy demand was estimated for 315 dwellings and an odds ratio analysis used to identify the socio-economic and dwelling factors that led to high electricity consumption. The effects of a number of socio-economic and dwelling factors which have not previously been studied for the UK domestic sector are included. Thus, for the first time, presence of teenagers, having electric space heating as the primary form of heating, portable electric heating and electric water heating were identified as significant drivers of high electricity demand in UK homes. The employment status and education level of the Household Representative Person, the number of floors in a dwelling, presence of fixed electric heating, and the proportion of low-energy lighting were shown to have no effect on high electricity consumption in UK homes. Given the impetus to reduce electricity consumption and CO 2 emissions from the domestic sector, these observations can help shape energy saving campaigns and future energy policy. [ABSTRACT FROM AUTHOR]

Improved methods for quantifying energy savings in buildings need to be supported by empirical measures rather than modeled estimates of future annual energy demand. This paper uses power temperature gradient (PTG, W/K), or the slope of power demand in response to changes in external air temperature; first, to categorise dwelling energy performance from daily energy data (when 0–15 °C outside); second, to investigate variations in 24-h profiles of delivered power. Estimates of PTG were obtained from 567 UK dwellings with 118,000 days of gas and electricity data. From a multivariable regression model, PTG was predicted by dwelling characteristics (number of bedrooms, number of floors, dwelling type, and dwelling age category (all p < 0.001)) but not by number of occupants. When dwellings were grouped into quintiles of PTG, mean PTG had threefold increase from the first to fifth quintile (188 to 563 W/K, respectively). This was reflected in 24-h profiles of delivered power (30 min intervals): at 0 °C, each 100 W/K decline in PTG corresponded to ∼2.5 kW decline in mean morning and evening peak power. Using PTG to estimate reductions in peak power as equivalent ‘negawatts’ reframes potential benefits of energy efficiency retrofits and for grid resilience. [ABSTRACT FROM AUTHOR]

Abstract: Historic dwellings in the UK make up to 20% of all homes. In the Georgian city of Bath this rises to 30%. These buildings are amongst the most poorly performing part of the English housing stock in energy use terms, with the lowest SAP rating and highest average annual CO2 emissions. The legal aim to reduce CO2 emissions of 80% by 2050 will involve all existing dwellings, including historic buildings. The degree to which proposals to retrofit the UK housing stock can reduce emissions depends on how much energy they currently use, what it is used for and how much CO2 they emit. This paper establishes a benchmark of energy use and CO2 emissions for historic dwellings in Bath. This permits comparison of their energy performance against other parts of the housing stock and will facilitate evaluation of potential retrofit adaptations. [Copyright &y& Elsevier]