Moisture in walls before and after internal wall insulation: A long-term in-situ dataset

This work addresses the need for long-term, high-resolution, in-situ datasets by providing in-wall humidity and temperature data from three walls of in-use office buildings over three to four years, two of which were insulated during this period. Temperature and humidity sensors were inserted diagonally into three locations within the thick brick walls, and the holes were carefully packed with dry brick dust. Surface, ambient and interstitial measurements were also recorded, as were additional climatic data during 18 months at one wall, and in-wall moisture content using resistance pins for 18 months in another. This dataset is unique due to the combination of temporal length and resolution, spatial detail, as well as the availability of control data from both before and after insulation and the use of real climatic conditions on both sides of the walls. The experiment was designed to produce data to facilitate parameter estimation by inverse analysis. By using these estimated parameters, or by taking material property measurements, it would also be useful for validating and calibrating hygrothermal models, and by using plausible ranges for parameters it is useful for exploring model performance, such as through sensitivity analyses

Characterization of the thermal structure of different building constructions using in-situ measurements and Bayesian analysis

A dynamic method, comprising a two lumped-thermal-mass model and Markov Chain Monte Carlo sampler, was used to analyze in-situ-monitored data and estimate the thermophysical properties of two walls of different construction. This method, unlike maximum a posteriori approaches, estimates the parameters’ probability distributions, providing insight into the wall’s thermal structure. Total R-values were well defined for both walls, whilst constituent estimated R-values for a solid wall having layers of materials with similar thermal properties were anticorrelated (thermal mass locations weakly constrained), but were not correlated for an insulated cavity wall with thermally distinct layers (thermal mass locations strongly thermally constrained)

Applying the CO₂ concentration-decay tracer-gas method in long term monitoring campaigns in occupied homes: identifying appropriate unoccupied periods and decay periods

PURPOSE: Ventilation is driven by weather conditions, occupant actions and mechanical ventilation, and so can be highly variable. This paper reports on the development of two analysis algorithms designed to facilitate investigation of ventilation in occupied homes over time. DESIGN/METHODOLOGY/APPROACH: These algorithms facilitate application of the CO2 concentration decay tracer gas technique. The first algorithm identifies occupied periods. The second identifies periods of decaying CO2 concentration which can be assumed to meet the assumptions required for analysis. FINDINGS: The algorithms were successfully applied in four occupied dwellings, giving over 100 ventilation measurements during a six-month period for three flats. The specific implementation of the decay identification algorithm had important ramifications for the ventilation rates measured, highlighting the importance of interrogating the way that appropriate periods for analysis are identified. PRACTICAL IMPLICATIONS: The analysis algorithms provide robust, reliable and repeatable identification of CO2 decay periods appropriate for ventilation rate analysis. The algorithms were coded in Python, and these have been made available via GitHub. As well as supporting future CO2 tracer gas experiments, the algorithms could be adapted to different purposes, including the use of other tracer gases or exploring occupant exposure to indoor air pollution. ORIGINALITY/VALUE: Empirical investigations of ventilation in occupied dwellings rarely aim to investigate the variability of ventilation. This paper reports on analysis methods which can be used to address this gap in the empirical evidence

A Bayesian dynamic method to estimate the thermophysical properties of building elements in all seasons, orientations and with reduced error

The performance gap between the expected and actual energy performance of buildings and elements has stimulated interest in in-situ measurements. Most research has employed quasi-static analysis methods that estimate heat loss metrics such as U-values, without taking advantage of the rich time series data that is often recorded. This paper presents a dynamic Bayesian-based method to estimate the thermophysical properties of building elements from in-situ measurements. The analysis includes Markov chain Monte Carlo (MCMC) estimation, priors, uncertainty analysis, and model comparison to select the most appropriate model. Data from two case study dwellings is used to illustrate model performance; U-value estimates from the dynamic and static methods are within error estimates, with the dynamic model generally requiring much shorter time series than the static model. The dynamic model produced robust results at all times of year, including when the average indoor-to-outdoor temperature difference was low, when external temperatures had large daily variation, and measurements were subjected to direct solar radiation. Further, the probability distributions of parameters may provide insights into the thermal performance of elements. Dynamic methods such as that presented herein may enable wider characterisation of the performance of building elements as built, supporting work to reduce the performance gap

Sensitivity and Uncertainty analyses on a DELPHIN model: The impact of material properties on moisture in a solid brick wall

This paper presents sensitivity and uncertainty analyses on a DELPHIN model, which is representative of a case study wall in real climatic conditions. Results of the Differential Sensitivity Analysis (DSA) show properties governing liquid water transported into, through and stored in the wall impact most on moisture accumulation, affecting relative humidity (RH) outputs by 10 – 35% at three different locations in the wall. Parameters affecting vapour transport into the room also influence RH outputs at the inner location, but less than rain amount and rain exchange coefficient. A probabilistic uncertainty study is then used to explore key material functions, parameterised as four sets of co-ordinates and varied randomly. The correlation between the parameter inputs and the resulting change in RH is assessed. There are some surprising divergences from the DSA, including the significance of moisture storage in the plaster layer in the presence of liquid. Low correlation coefficients suggest numbers of variables could be reduced to further clarify the effects of these parameters, and interesting questions are raised on the parameterisation of material functions to represent the uncertainty in the characterisation of real walls

Knowing me, knowing you: the role of trust, locus of control and privacy concern in acceptance of domestic electricity demand-side response

Choosing to take part in a demand-side response (DSR) programme entails accepting external influence over one’s energy consumption patterns, such as through price or direct load control (DLC) signals. If participation is low, the programme will be ineffective. How might people’s perceptions of their relationship with the influencing entity affect the likelihood of participation? This study used a representative survey of Great Britain (N=2002) to explore the importance of trust, privacy concern and locus of control for acceptance of different approaches to influencing electricity consumption. Survey respondents were randomly shown a description of one of five DSR products (static time of use [TOU] tariff, static TOU with automated response to price changes, dynamic TOU, dynamic TOU with automated response, and DLC), framed as being offered by their electricity supplier. They then responded to a number of scales including those intended to measure trust in their supplier, privacy concern and locus of control. Controlling for demographic variables, trust in electricity supplier was significantly positively associated with acceptance of all tariffs, although the effect size was smaller for the automated TOU tariffs. The specific measure of trust in the supplier to ensure a reliable electricity supply was significantly negatively associated with acceptance of the dynamic TOU tariff. Privacy concern was significantly negatively associated with acceptance of all tariffs, with the strongest effect for the automated dynamic TOU tariff. Locus of control was a significant factor only in the case of DLC, where external locus was related to higher acceptance. These results suggest the existing low levels of trust in energy companies in the UK may present a challenge in securing uptake of DSR, and an opportunity to trusted entrants from other sectors. Automation within the home may mitigate trust concerns, but people must have confidence in the privacy of this arrangement. DLC may be viewed especially positively by people who currently perceive themselves to have little control over their energy use, but protections should be in place to ensure they are not exploited

Determining the impact of regulatory policy on UK gas use using Bayesian analysis on publicly available data

This paper presents a novel method to analyse policy performance, using the example of legislation in the UK to require domestic boilers fitted since 1 April 2005 to be condensing. A technological uptake model based on the logistic equation is combined with four physical and economic models; Bayesian techniques are used for data analysis. Projections of energy savings are presented and the impact of different policy implementation dates investigated

Long-term in-situ measurement of heat flux and temperature on a filled cavity wall in a residential building in the UK

Long-term (over five months of data) in-situ measurements of heat flux and temperature on a north-facing filled cavity wall located at the ground floor of a 1970s unoccupied residential building in Cambridgeshire (UK). This dataset is described in: Gori, V., & Elwell, C. A. (2018). Estimation of thermophysical properties from in-situ measurements in all seasons: quantifying and reducing errors using dynamic grey-box methods. Energy and Buildings. Please cite this publication when using and referencing this dataset

Long-term in-situ measurements of heat flux and temperature on a solid-brick wall in an office building in the UK

Long-term (one year of data) in-situ measurements of heat flux and temperature on a north-west-facing solid-brick wall located on the first floor above ground of an office building in central London (UK). This dataset is described in: Gori, V., & Elwell, C. A. (2018). Estimation of thermophysical properties from in-situ measurements in all seasons: quantifying and reducing errors using dynamic grey-box methods. Energy and Buildings. Please cite this publication when using and referencing this dataset

The thermal characteristics of roofs: policy, installation and performance

This paper investigates the in-situ performance of UK cold pitched roof structures through a case study dwelling of typical construction using site survey, and estimation of U-values through simple calculation and from measured heat flow data. Significant increases of U-values resulted from under- and un-insulated areas due to installation issues, whilst a higher than expected estimated thermal resistance of the roof space and structure was also noted, potentially associated with heat gains. Both issues are expected to be observed more widely in the stock and contribute to a performance gap for roof insulation