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

Abstract

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.

Creating Domestic Building Thermal Performance Ratings Using Smart Meter Data

“…Overcoming the limitations described above may be possible through the use of dynamic methods, which aim at explicitly modelling heat transfer and storage in the building. Whilst several dynamic frameworks have been developed to characterise the thermophysical performance of unoccupied buildings (Bauwens and Roels 2014; Mangematin, Pandraud and Roux 2012;Palmer et al 2011;Subbarao et al 1988;Thébault and Bouchié 2018), little is currently available in the literature in relation to dynamic methods for the characterisation of occupied dwellings (Fonti et al 2017;Harb et al 2016;Hollick, Gori and Elwell 2020). As reported by Harb et al (2016), robust models that separate out the unpredictable influence of occupants on the internal environment (e.g., free gains due to both internal and solar gains, or air-change rates due to window interaction) are still uncommon.…”

“…Simplified yet robust frameworks requiring a small number of data inputs collected in occupied dwellings would be very valuable in the light of characterising whole-building thermal performance for energy performance rating purposes. Harb et al (2016) and Hollick et al (2020) incorporate a limited amount of additional information beyond smart meter data, such as internal temperature and geographical location (from which, in turn, solar radiation and external temperature can be retrieved). Notably, Hollick et al (2020) developed several lumped capacitance models of occupied dwellings explicitly including gains from solar radiation with varying complexity, allowing the estimation of the HTC or the HPLC and the solar aperture from short time series collected at all times of the year (including summer) by means of an inverse grey-box framework.…”

“…Incorporating more data streams may reduce uncertainty. Hollick et al (2020) achieved a (yearly average) uncertainty of around 6% using extra data inputs consisting of several internal temperatures, solar radiation from a nearby weather station and dwelling orientation. Note that the literature is not yet clear on exactly how uncertainty on a rating is defined, for example, whether it consists of the fitting error of the algorithm or a wider definition including inherent variability of the rating itself over the year (Hollick et al 2020).…”

“…An alternative and less intrusive characterisation of window opening is to assume that it causes all observed seasonal variation in the HPLC or HTC. However, this is not a robust assumption as shading and solar gain also affect these metrics differently throughout the year (Hollick et al 2020).…”

Automated Generation of an Energy Simulation Model for an Existing Building from UAV Imagery

“…Their drawbacks are that they cannot be the only source of information for individual envelope component refurbishment, that solar gains and air exchange need to be accounted for with additional measurements and modelling effort, and the need for an unoccupied building. If such dedicated measurement campaigns should be avoided, some available approaches are the energy signature method [16], grey-box regression [17], or modelling based on data from smart meters or building automation systems [18,19]. Similar approaches can also be used to calibrate existing models [20].…”