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Yes / Detailed electricity load profile for domestic building is an important requirement for the accurate analysis of demand side management. The use of electrical appliances within domestic buildings varies significantly with respect to time, mainly in accordance with the activity and behaviour of the occupants. This paper presents results from a monitoring study of electrical energy consumption profiles for One UK household (two adults with children). Measurements for whole household electricity consumption have been obtained over a period of ten months. They were all obtained at one minute interval. Monthly energy consumptions, daily and overall profiles were derived for this household type from the monitored data. It is intended that the results presented in this paper can be used in the quest for a precise forecast method for electricity consumption for occupants living in the same type of household in the UK. This will allow greater confidence in the sizing of, e.g., adopting renewable energy sources in this type of household. Further investigation is needed for a large sample of households to improve the understanding of monitoring high resolution domestic energy consumption. / MSCRC

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Yes / This paper presents a method of generating realistic electricity load profile data for the UK domestic buildings. The domestic space heating and domestic hot water have been excluded in this study. The information and results of previous investigations and works that is available in public reports and statistics have been used as input data when modeling of domestic energy consumption. A questionnaire survey was conducted to find out what occupants do in different times of the day in order to get probabilistic estimates of usage of electrical household. The daily energy demand load profile of each appliance can be predicted using this method. A measured data set is also applied for comparison, and verification. Our analysis shows that the generated load profiles have a good agreement with real data. The daily load profile from individual dwelling to community can be predicted using this method.

The amount of electricity used in individual UK homes varies considerably. Previous UK energy research has identified that high electricity consuming homes not only use more electricity, compared with others, but appear to be consuming even more electricity over time. Furthermore, there is additional evidence which shows that high consuming dwellings also have a greater potential to make energy savings than those who consume less. It has been suggested that future UK energy policy might focus on reducing the demand of high electricity consumers in order to reduce overall CO2 emissions. Therefore, understanding what drives high usage in domestic buildings is essential to support informed decisions. This thesis asserts that to improve knowledge and understanding of the factors affecting high electrical energy consumption in UK domestic buildings, it is necessary to combine an analysis of the occupants socio-economic characteristics, dwelling technical characteristics and appliance related aspects, with detailed monitoring of the ownership, power demand and occupants use of electrical appliances. Using a sample of 315 UK homes, the influence of socio-economic, technical and appliance related characteristics on the probability of a household being a high electrical energy consumer was investigated (Odds ratio analysis). Detailed appliance monitoring data was collected from 27 UK homes to establish the contributions of appliance ownership, power demand and use to high electrical energy demand (Appliance Electricity Use Survey). The current research found similar skewed electricity distributions towards high electricity consumers for both the 315 and 27 home cohorts. Conflicting results were however obtained from the two household samples with regard to whether high electricity consumers are increasing electrical energy demand over time. The results of the odds ratio analysis and Appliance Electricity Use Survey suggest that high electricity consumption in domestic buildings is related to a combination of the socio-economic characteristics of the building occupants, technical characteristics of the dwelling and the ownership, power demand and use of electrical appliances.

The successful delivery of low-carbon housing (both new and retrofitted) is a key aspect of the UK s commitment to an 80% reduction in carbon emissions by 2050. In this context, the inclusion of small-scale building-integrated renewable energy technologies is an important component of low carbon design strategies, and is subject to numerous regulation and incentive schemes (including the Renewable Heat Incentive (RHI)) set up by government to encourage uptake and set minimum performance benchmarks. Unfortunately there are numerous examples of in-use energy and carbon performance shortfalls for new and retrofitted buildings this is termed the performance gap . Technical and human factors associated with building subsystem performance, which are often not considered in design tools used to predict performance, are the root cause of performance uncertainty. The research presented in this doctoral thesis aims to develop and apply a novel probabilistic method of evaluating the performance uncertainty of solar thermal systems installed in the UK. Analysis of measured data from a group of low carbon retrofitted dwellings revealed that the majority of buildings failed to meet the designed-for carbon emissions target with an average percentage difference of 60%. An in-depth case study technical evaluation of one of these dwellings showed significant dysfunction associated with the combined ASHP/solar thermal heating system, resulting in a performance gap of 94%, illustrating that the performance gap can be regarded as a whole-system problem, comprising a number of subsystem causal factors. Using a detailed dataset obtained from the UK s largest field trial of domestic solar thermal systems, a cross-cutting evaluation of predicted vs. measured performance similarly revealed a discrepancy with a mean percentage difference in predicted and measured annual yield of -24%. Having defined the nature and extent of underperformance for solar thermal technology in the UK, causal factors influencing performance were mapped and the associated uncertainty quantified using a novel knowledge-based Bayesian network (BN). In addition, the BN approach along with Monte Carlo sampling was applied to the well-established BREDEM model in order to quantify performance uncertainty of solar thermal systems by producing distributions of annual yield. As such, the modified BN-based BREDEM model represents a significant improvement in the prediction of performance of small-scale renewable energy technologies. Finally, financial analysis applied to the probabilistic predictions of annual yield revealed that the current UK RHI scheme is unlikely to result in positive returns on investment for solar thermal systems unless the duration of the payments is extended or electricity is the primary source of heating.

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