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Home energy retrofit has recurred in public policy throughout recent decades. However, the savings in energy usage attributable to home retrofit have remained difficult to accurately predict. Occupants cause prediction inaccuracies by varying different factors, especially heating setpoints temperatures and heating patterns. Acting together, such occupant factors result in distributions — not single values — of heat-energy usage, even among similar homes. Datasets of heat-energy distributions can be found by building performance simulation using modern grey-box models. This study presents a methodology to simulate grey-box models of home heating through ranges of heating setpoints and patterns. An entire process to calibrate, validate and simulate at a large scale is described, and then demonstrated using case studies. Grey-box models, written in Modelica language, can conveniently simulate through large ranges of occupant factors. The case studies exploited this advantage of grey-box models to simulate empirical data on occupant factors. (For instance, empirical data found that home heating setpoints shifted before and after home energy retrofit.) In doing so, the datasets of simulation results enabled the exploration of home heat-energy usage with the normal and Weibull statistical distributions. Additionally, the heat-energy distributions of case-study homes were statistically tested, first for retrofit savings, second for equality to each other and third for equality to an official heat-energy estimate. Results demonstrate that home heat-energy usage, at a large scale, is best expressed as a Weibull distribution not normality. After home energy retrofit, heat-energy usage displays less variation (in general), less skewness, and thus becomes closer to normality. Occupant factors were found to vary home heat-energy usage into distinct distributions, even within similar homes. Therefore, in most case-study homes, heat-energy usage did not equal an official estimate. Finally, shallow retrofit of a modern home in Ireland fails to save heat-energy usage by most occupants. [ABSTRACT FROM AUTHOR]

Research into home energy retrofit is important because most existing homes will operate in 2050. A lack of funding or incentives often prevents home energy retrofit, particularly of social housing. This study analysed retrofitted Irish social housing and their gas meter data, including pre-payment meters that require regular “top-ups” purchased from shops. The data comprised records from 100 retrofit and control group homes throughout 2013–2015. A novel evaluation of retrofitted rented homes processed meter data into multiple metrics. Gas consumption is computed per house and weather correction is incorporated, enabling statistical testing of the retrofit. A “difference in difference” technique compared the retrofit and control groups. Gas consumptions of the most popular building type are plotted as distribution curves before and after retrofit. Subsequently the energy use intensity (kWh/m 2 /year) is computed per home; leading to calculation of the prebound effect. In social housing, the prebound effect quantifies energy underconsumption due to self-rationing. Retrofit significantly reduced gas consumption, and reduced its variance among homes. A small positive skewness in the statistical distribution of home gas consumption prevented characterisation as a normal distribution. The prebound effect is high, but alleviated by the retrofit. Finally, retrofit extended average pre-payment intervals. [ABSTRACT FROM AUTHOR]

• Relative and absolute home retrofit targets are reviewed and discussed. • Retrofit depth is determined by policy targets or practical cost-effectiveness. • Research studies find feasible energy savings of around 60% maximum. • Neighbourhood is optimal spatial scale to large-scale deliver home energy retrofit. • Future retrofits to be phased instead of one-off, and less primacy of fabric first. Home energy retrofit has been a publicly-funded policy in many countries since the 1990s. Government motivations have spanned from combating energy poverty to climate action and sustainability. Given the ambition of public policy, this paper reviews the targets, spatial scales and metrics used to deliver and evaluate home energy retrofit. Large-scale retrofit projects do differ: by spatial area, by retrofit depth and in evaluation metrics (if used). This review surveys first, the quantities of homes and inhabitants in intermediate spatial scales used to plan retrofit programmes. Second, the optimal spatial scale and retrofit depth to deliver large-scale home retrofit. Third, the quantitative metrics to evaluate retrofits, and future trends in occupant-based metrics. Neighbourhood is found to be the optimal spatial scale for home energy retrofit. Deep retrofit is described as 60% energy savings that, in synergy with energy supply decarbonisation, promises to deliver 80% or higher emission reductions. Recent evidence shows a shifting primacy from fabric-first retrofit to decarbonisation of heat generation and occupant health and well-being. [ABSTRACT FROM AUTHOR]

• TABULA home archetypes are white-box modelled with realistic occupant variables. • Model parameters narrow the gap between calculated and measured energy use to 5–10%. • In temperate climates, measured heat energy is 22–32% below steady-state simulation. • Deep retrofit of an old home with modern boiler achieves up to 60% energy savings. • Shallow retrofit of a modern home achieves climate action target with heat pump only. Recent climate action plans set ambitious targets for large-scale home energy retrofit. These retrofit targets also recommend switching from oil boilers to electrical heat pumps. One area-based approach to retrofit is neighbourhood scale. Ideally with local community participation, neighbourhood retrofit improves a group of structurally similar homes, of the same archetype. Defined home archetypes already exist for 20 European countries thanks to previous research. That same research attempted steady-state calculations of space heating energy using the ISO 13790 Seasonal Method. Those calculations diverged from measured energy use by a large gap. Taking example home archetypes from Ireland, this study narrowed this energy-use gap by incorporating realistic parameters and schedules. Physical white-box models were constructed in DesignBuilder, and simulated by EnergyPlus with local weather data. The simulations combined realistic heating schedules, internal gains, infiltration and ventilation; converging simulation energy use with typical measurements and mostly achieved model calibration indices. Therefore, the resultant energy-use data was capable of tuning future grey-box models to investigate home energy use across ranges of heating patterns and heating setpoints. The aims of this study are threefold. First, devise a repeatable method to produce heat energy-use values by simulating white-box models of home archetypes, that enable tuning of future grey-box models. Second, specify the heating variables that result in acceptable energy-use gaps between simulation of home archetype models and their typical measurements. Third, test if the retrofits of three Irish home archetypes under a typical occupant schedule, achieve national climate action targets. [ABSTRACT FROM AUTHOR]

Developing BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching. [ABSTRACT FROM AUTHOR]