The accurate determination of the in-use heat transfer coefficient (HTC) of a dwelling can support efficiency improvements and understanding of energy costs, potentially addressing the performance gap. This paper introduces a dynamic grey-box framework combining Bayesian methods and lumped thermal capacitance models for the estimation of the performance of in-use buildings. It focuses on methods to account for solar gains, a significant contributor to the heat transfer. Six simple first-order lumped models of occupied homes are presented, which explicitly include gains from solar radiation with varying complexity. Specifically, the models use solar radiation as a single heat input, divided by façade according to the angle of the sun, and including diffuse radiation. Two case study houses in the UK, monitored over two different seasons, were used to illustrate the models' performance. Bayesian model comparison was used, in conjunction with other methods, to determine the most suitable model for each sub-dataset analysed; this indicates that the most appropriate model is both season and case-study dependent, highlighting the importance of local topography and weather experienced. For each case study, the models selected provided HTC estimates within 15% of each other, including during the summer, using only 5-10 days of data. Such techniques have the potential to estimate the thermal performance of dwellings year-round, with minimum disturbance to the occupants and could be developed to improve quality assurance processes for new build and retrofit, identify opportunities for targeted retrofit, and close the performance gap.
To contain the spread of Covid-19, governments across the world imposed partial or complete lockdowns. National energy demand decreased in periods of lockdowns; however, as people spent more time at home, residential energy use likely increased. This paper reports the results of a UK survey study (N = 1016 participants) about their energy-use practices during the first lockdown in March 2020. The results indicated that self-reported heating behaviours did not substantially change during lockdown. Regarding appliance use, in particular the duration of usage for televisions and computing equipment has increased and has spread more over the day. Being less able to manage financially was correlated with a greater usage of the smart meter in-home display and a greater attempt to save energy was positively correlated with greater usage of the in-home display, though correlations were small. In summary, the results indicate that home energy-use behaviours, in particular around heating, did not change as much as might have been expected, which might at least partly be explained by the comparatively warm weather during the first lockdown. Corroborating the survey findings with actual energy data is the next essential step to understand findings in more detail. POLICY RELEVANCEGovernments are developing policies to support the transition to net zero. Covid-19 has accelerated the transition in behaviours such as home working which may result in a 'new normal' energy behaviour and will need to be taken account when planning for net zero. Insights into the changes in behaviour during lockdown indicate it would be oversimplified to assume that electricity and gas use have increased in all homes because of a stay-athome order. Self-reported heating did not change, whereas electrical appliance usage increased. The sample composition of the household is important for understanding
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