Thermal performance of occupied homes: A dynamic grey-box method accounting for solar gains

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 facade 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 510 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. (C) 2019 Elsevier B.V. All rights reserved.