UNCERTAINTY QUANTIFICATION
Uncertainty quantification is a term used to refer to the science of quantitative reduction and characterisation of uncertainties in the real and computational world applications (Smith, 2013). Uncertainty quantification tries to determine the probability of particular outcomes when some of the system aspects are not known. Recently, uncertainty quantification has received massive developments intending to achieve mitigation of risk through scientific prediction. It has resulted in the integration of ideas grasped from engineering, mathematics, and statistics, which are used in lending credence towards the predictive assessments of risks as well as designing the actions of investors, engineers, and scientists that are reliable with risk aversion.
In a modelling study by Hamilton and other scholars concerning the health effects of home energy efficiency interventions within England, the authors mainly aimed at assessing the potential public health impacts that resulted from changes in the indoor air quality as well as temperature as a result of energy efficiency retrofits within English households to meet the targeted carbon reduction by 2030 (Hamilton et al. 2015).
Indoor Environment of the Model
The model developed in the study was characterised by the 2010 indoor environmental situations within the English Housing Survey (Hamilton et al. 2011). The conditions and changes within those conditions in the indoor environment related to energy efficiency intercessions were modelled by the use of validated building airflow and physics models. The modelling that was described elsewhere in detail, utilised representative dwelling forms that were informed through sampling from the English Housing Survey so that it could represent the English household stock. Every archetype was modelled on differing levels of airtightness as well as ventilation systems, including the combined use of extract fans and trickle vents, window opening only, extract fans as well as window trickle vents.
The archetypes were modelled and matched to the English Housing Survey based on the household type, either flats, detached, terraces, and semidetached, the notional permeability, as well as the floor area, were 896 (Hamilton et al. 2015). The outcome of this was a model of indoor environmental conditions for a representative sample of English households. The home energy performance was contrived as a notional heat loss value. An empirical relationship was used between the standard internal temperature and the home heat loss value to predict the living room and the bedroom temperature standardised at 5 degrees external temperature.
The standardised internal temperature is the thermal condition of home measure that is ranked counter to all other homes or dwellings (Willand, Ridley and Maller, 2015). Also, it is a function of the energy of the house as well as the ventilation performance. The standardised internal temperature towards thermal performance relationship that was utilised in the model helps in capturing the empirical rebound in temperature such as the temperature increases, reduced heat flow as well as changes within occupant heating practices. The authors utilised the English Housing Survey data on household fabric characteristics, the type of heating system as well as the presence of ventilation systems that would help in determining the eligibility of energy efficiency upgrades (Hamilton et al. 2015).
Uncertainty Analysis
In the modelling, Monte Carlo simulation was used in the assessment of parametric uncertainty within the health impact related to the determinant of the utility weights, the response-exposure relationships as well as complete exposure change of each health outcome (Smith, 2013). This means the heat loss change as well as airtightness resulting from each intervention. Hamilton et al. reported estimates of 95% credible interval, which were based on the 97.5th and 2.5th centiles of the outcomes that were generated from 500 model interactions (Hamilton et al. 2015). The model was also examined on the uncertainty resulting from two significant structural assumptions which are the toxicity of the particles that came from the indoor sources as well as the lives lost length within those who die out of cold-related causes.
The chronic health impacts were assessed for the cold by the use of response-exposure functions according to time series analysis implied that the individuals vulnerable to risks related to cold have a similar life expectancy as the average population. This could not be the case to some extent since there is a likelihood that those individuals who died out of cold events are those individuals who have a life expectancy shorter than the average population. This was addressed by assessing the assuming effect that those at high-risk are vulnerable to a cold subgroup in a population comprising higher underlying cardiovascular risk.
The shortening of remaining life expectancy of the high-risk subgroup was examined as a function of the advancement of risk within a group that is high-risk in comparison to the remaining population as well as the size of the subgroup as a proportion of the entire population. In the case of particle toxicity, the epidemiology was subjected by outdoor air pollution studies (Hamilton et al. 2015). Nevertheless, it is not clear as to whether the toxicity should be assumed for those particles that were derived from indoor sources which as a result of an increase in airtightness, the concentration could rise. This uncertainty was accounted for by performing calculation with as well as without including the approximated effect of those particles that were derived from indoor sources.
Cold-related Death Risk Group Size
The reduction of the size the group of cardiovascular individuals who are at high-risk from the population helps in the reduction of the health benefits scale as a result of improved winter temperatures even though the overall impact is modest. This is illustrated by concentrating the risk transverse increasingly in small proportions of the population that are selected to characterise the full range of reasonable assumptions. A postulation of 100% of the excess winter cardiovascular deaths within the high-risk group (i.e., the entire population is at risk) possibly will result in a substantial miscalculate of the change within the burden of wintertime cardiovascular illness, whereas estimation of 0.1% (i.e., only 0.1% of the populace are at risk) would efficiently eradicate all of the possible advantages of augmented temperatures for the people’s health (Hamilton et al. 2015). Awaiting additional research, it is hard to approximate the precise level of modification. Nevertheless, the influence is nearly sure to be substantially less than that oblique by utilising time-series constants used without any rectification.
The Toxicity of Indoor Particulate Matter
There exists relative toxicity of particles that is usually produced from the indoor sources as compared to those from outdoor sources. The analysis of the indoor-generated PM2.5 that assumed that there was no opposing impact on health had a significant impact of reducing the general net health effect by about 78% in comparison to the base case results (Hamilton et al. 2015). However, there was uncertainty effect, there was the possibility of some impact resulting from the indoor sources, and hence there would be a need to stress for the lack of more empirical studies which assess and measure the indoor PM2.5 toxicity as well as the balance of the outdoor and the indoor particles on health.
The Multiple-Criteria Decision Analysis is an analysis that helps in the decision-making process which evaluates several criteria as being part of the decision-making process. In comparing the regulation, installer discretion as well as no added ventilation across the indoor exposures in the model, the following steps of the Multi-Criteria Decision Analysis will be applied. First is by defining the context, identifying all the available options, determining the aims as well as selecting the best criteria that represent the value (Smith, 2013). The other step is the measurement of each of the criteria to discern their comparative significance, then calculate the diverse costs through averaging scores and weight.
Assuming that the model is used to minimise the annual heating energy consumption through the manipulation of several building characteristics while at the same time ensuring the environmental exposures such as indoor PM2.5 or the standardised indoor temperature are fewer constrained within lower and upper bounds, a tremendous mathematical strategy would be effective (Rakib et al. 2017). Designing of buildings in a way that the users’ expectations are in no small extent satisfied with passive measures, without any other energy source other than the sun and the environment could effectively improve energy efficiency and in the same time not reducing the indoor environment quality. This could be through thermal insulation, passive solar heating in the cold seasons, passive cooling during the warm season, thermal inertia, moveable solar protections, stack or natural ventilation as well as daylighting.
To also reduce the consumption of energy, the general controls optimisation, audits, commissioning as well as retrofits would help significantly. The best strategy, therefore, is to design energy-efficient buildings that satisfy all the requirements of indoor environment without or using little energy coming from the environmental control (Wang, Kuckelkorn, and Du, 2019). Reducing the need for mechanical cooling and heating through the design of a high-performance building envelop and install high-efficiency energy systems if required. At the same time, that are well-sized and controlled. In winter, to reduce heat loss, the best way would be by conduction and ventilation. In the climatic conditions where summers start becoming critical, first, it would be essential to reduce the heat fluxes that enter the building, which could cause internal energy hence indoor temperatures. In a high humidity climate, ventilating as much as possible would be essential to ensure all the moisture-laden air is expelled, and this can also be done by installing a dehumidification system.
Reference List
Hamilton, I., Milner, J., Chalabi, Z., Das, P., Jones, B., Shrubsole, C., Davies, M. and Wilkinson, P., 2015. Health effects of home energy efficiency interventions in England: a modelling study. BMJ open, 5(4), p.e007298.
Hamilton, I.G., Davies, M., Ridley, I., Oreszczyn, T., Barrett, M., Lowe, R., Hong, S., Wilkinson, P. and Chalabi, Z., 2011. The impact of housing energy efficiency improvements on reduced exposure to cold—the ‘temperature take back factor’. Building Services Engineering Research and Technology, 32(1), pp.85-98.
Rakib, M.I., Saidur, R., Mohamad, E.N. and Afifi, A.M., 2017. Waste-heat utilisation–the sustainable technologies to minimise energy consumption in Bangladesh textile sector. Journal of cleaner production, 142, pp.1867-1876.
Smith, R.C., 2013. Uncertainty quantification: theory, implementation, and applications (Vol. 12). Siam.
Wang, Y., Kuckelkorn, J.M., Li, D. and Du, J., 2019. A novel coupling control with decision-maker and PID controller for minimising heating energy consumption and ensuring indoor environmental quality. Journal of Building Physics, 43(1), pp.22-45.
Willand, N., Ridley, I. and Maller, C., 2015. Towards explaining the health impacts of residential energy efficiency interventions–A realist review. Part 1: Pathways. Social science & medicine, 133, pp.191-201.