An Expert View on Data and Modelling for Planning Domestic Retrofit
Abstract
:1. Introduction
2. Data and Modelling in the Retrofit Process
2.1. The Retrofit Process
2.2. Data and Modelling
3. Materials and Methods
4. Results
4.1. Characterisation of Study Participants’ Professional Experiences with Retrofit Data and Modelling
4.2. Thematic Analysis
4.3. Trade-Offs between Precision, Confidence, and Burden of Collection in Data and Modelling
“I’m reasonably confident things like smart meter data have to be right on the whole, they’re not 100%, but 99.99% you get the right data from there because it’s so standardised and regulated”.P2
“I do [have confidence] if it’s the right model and I’ve been sufficiently in control of what’s gone into that model”.P8
“I think it’s just easier in SAP or rdSAP to put the default value in or use an assumed value because there’s a lot of guidance on that as well… If you’ve got something that’s constructed in 1950 and it’s made of cavity wall, here’s the U Value you can use. You go oh great I’ll just use that. You know you’re not quite thinking, is it a really thick cavity or is it a really thin cavity?”P1
“The heat flux plates are the most reliable ones we’ve found. But the problem is then: Is a wall uniform all the way along it for the U value? And then actually it probably isn’t. So how many heat flux plates do you need? […] And then you just end up in an absolute data nightmare […] That’s the person who has the passive house. And we can put all these things on your walls and do U values. And she’s like, “does it damage the walls?” [Answer] “Well it might leave a sticky mark” You might need to redecorate”. You think, if that’s just from one test of your house over a weekend. When we test for the co heating you heat someone’s house up to 25 degrees, you don’t want that. You can’t live like that and it’s too expensive to move them out”. […]P4
“Where you’ve got social landlords, it’s alright focusing on the needs of the current occupant, but there’s nothing to say that they’re going to be living there in two years. […] You’re wanting to [design] for the conditions of your average person who’s going to be renting that dwelling, not just one person who might have like idiosyncrasies in the way that they use that property”.P6
“We tend to do more building performance [tests]: ‘Does the building work?’ rather than ‘What’s the occupant doing?’ But we used to do that, we would monitor buildings for 18 months and then two years. We are doing less that and doing more physical building performance type stuff. So pre and post [retrofit], because […] they are very hard to manage”.P2
4.4. Who Accesses the Data and Modelling Results in Retrofit?
“And I think when we’ve been doing the building work, what’s been most useful to the contractors is our thermal images. So, they want to know that, you know, cause what, so we’ve been doing like air tightness retrofits on some properties, which basically just involves the contractor, trying to just bung up as many holes as possible in the leaky house we were doing. Um, but you know, if they could see where the gaps are, where the leaks are, it helps”.P4
“We found that, you know, bits weren’t working quite like they were expected. […] So, we’re in the process of resolving that. So, making sure that the heat pump gets switched out for one that is as efficient as the design said it was going to be. It’s their responsibility because there’s the measured data to be able to say that that wasn’t as good as the design said”.P1
“It [would] be nice to be able to generate that sort of [results of model] in 3D or even 2D without having to learn an entirely new skill […] It would just be to be able to more accurately show a client where the weaknesses are in a dwelling. And thus, be able to better target interventions. […] So, some of the local authorities are going around trying to do thermal imaging as an engagement process with their residents. So, they’re planning to go down the street in the winter and go, “Oh, looks like you’ve got a few leaky bits on your house, would you like to sign up to our retrofit scheme?”P7
“What are we doing about teaching people about how they can utilise their solar PV better are we just putting it in? And they just get home from work and at seven o’clock, they’re sticking their washing machine on because that’s what they’ve done. And they’re not utilising the free energy. You can’t change people’s behaviour unless they know why they should be doing it. And if you can take them through that process, you can help them make an impact”.P3
5. Discussion
Limitations
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Definition | Examples | |
---|---|---|
Retrofit Data | True and measured information regarding the houses being retrofitted. | Such as the results of metered energy use and cost inflows/outflows. |
Retrofit Model | A model that produces a representation or simulation of energy consumption before/after retrofit; material use; cost of retrofit; and other building performance metrics. | Such as building energy models (like SAP, EnergyPlus) or financial modelling. |
ID | Job Title | Stakeholder Category | LETI Stage (s) |
---|---|---|---|
P1 | Retrofit consultant and net zero technical analyst | Consultancy | 1, 2, 3 |
P2 | Director of research group | Research Institute | 2, 3 |
P3 | Programme manager—retrofit | Housing Partnership | 1, 2, 3, 4, 5 |
P4 | Research assistant | Research Institute | 1, 2 |
P5 | Data and information manager | Public Sector Body | 1, 2 |
P6 | MCS manager and retrofit coordinator | Community Interest Company | 1, 2, 3, 4, 5 |
P7 | Retrofit assessor | Social Enterprise | 2 |
P8 | Joint head of sustainability | Architecture firm | 2, 3 |
Name | Definition |
---|---|
Quantitative Data | Measurements and direct observations concerning the building and its surrounding. |
Observational Data | External factors that influence the building. |
Building Performance Data | Data related to how the building is functioning. |
Building Survey Data | The physical features of the building. |
Models | Simulation software and planning tools used in the retrofit process. |
BIM | Building information modelling (BIM) uses various tools and technologies to store a lot of information within a 3D model. |
DSM | Dynamic Simulation Models (DSM) model dynamic processes. For example, Energy Plus simulates energy consumption over time, in the context of energy efficiency. |
Question Data | Findings from interviews and questionnaires with occupants and stakeholders. |
Theme | Definition | Examples | Quote | I | E |
---|---|---|---|---|---|
A. Data and modelling are [not] trustworthy if… | Participants find the quality of secondary data and modelling results used to inform retrofit actions and input into models to be poor. Participants only have confidence in data and modelling under certain conditions. | Untrustworthy data and models included inaccurate EPC ratings; outdated costing data limited to certain typologies. Trustworthy data and models included data collected under controlled conditions; regulated and standardised data collection equipment; and data collected by a trusted surveyor; where only a subset of retrofit measures are modelled. | “There’s a pre-EPC calculation and the difference might be one or two EPC bands commonly”. P5 | 8 | 10 |
B. Data increases confidence in retrofit work | Participants collect, analyse, and communicate data before and after retrofit to increase the confidence of clients, occupants, and themselves in the retrofit work carried out. | E.g., thermographic images facilitate communication between residents, financial stakeholders, and contractors before retrofit; energy use data ensure that discrepancies post retrofit can be addressed, such as poor occupancy behaviour and MEP performance. | “I’ve used [thermal imaging] to show that cavity wall insulation has degraded or slumped because you can see a change in temperature in a wall”. P7 | 6 | 11 |
C. Better data collection methods are needed to help inform retrofit actions | Participants find current data collection methods are invasive, unreliable, and unacceptable to occupants, limiting opportunities for participants to collect usable data to inform retrofit actions. | E.g., drop-out data due to malfunctioning technology; invasive methods of measurement such as U-Value tests leaving marks on walls; using a drill on the wall to better understand the building fabric. | “There often is dropout data [with data monitoring systems] or a bit of data gets lost or there is a big spike or something in the data. And I find I always have to do a bit of checking of the data and deleting those kinds of outliers. But […] where’s the limit of is this an outlier bit of data or is this actual data?” P1 | 5 | 13 |
D. More opportunities to input specific data into models are needed to increase model accuracy | Participants believe there are not enough opportunities to input highly specific data into retrofit models leading to the use of outdated inputs in models. | E.g., rdSAP/SAP carbon predictions are inaccurate due to outdated carbon factors; rdSAP/SAP provide assumptions such as the performance of built elements which impacts the accuracy of results. | “One of the things that’s been wrong with the SAP data is the carbon intensity estimates of electricity. They haven’t really changed for quite a long time, yet, we’ve decarbonised our electricity supply quite a lot. So, things like heat pumps will come out with worse performing SAP points because SAP is still using estimates for electricity being generated from gas, oil, coal”. P3 | 5 | 11 |
E. Data and models are used to target retrofit measures within the financial constraints | Participants discussed using data and models to target which homes to retrofit and what retrofit measures to implement to best use a limited budget. | E.g., EPC certificates are used to target particular houses which are eligible for funding; rdSAP model is used to target particularly effective measures. | “Because the cost of installing measures has gone up and the funding from the treasury hasn’t, we have to also look for perhaps areas which won’t all be very large attached houses because that’s very expensive to retrofit”. P5 | 5 | 9 |
F. Occupant-related data impacts retrofit work | Participants change the retrofit design based on occupancy-related information. | E.g., occupants have different priorities, such as the aesthetics of wall insulation, which informs the retrofit measures; whether the property is owner-occupied or social housing impacts the retrofit measures implemented; when undertaking a PHPP model changing the occupancy patterns greatly impacts the results. | “We as a design team were being asked to guarantee the performance of the finished building. And it became apparent in our PHPP model that if you change the occupancy, you radically change particularly the risk of overheating, which is actually the bigger challenge. So, it became apparent that it was important that we actually found out how many people live there, not just assumed, based on the floor area or the bedrooms”. P8 | 4 | 5 |
G. Data and modelling could help inform and educate occupants (speculative) | Participants discuss how occupant behaviour can undermine the impacts of retrofit measures. Participants identify a lack of education of occupants on retrofitting measures and show that data collection and monitoring can be used to assist occupant education. | E.g., occupants not knowing how to use the new equipment (thermostat) in their homes, resulting in higher energy usage than predicted; occupants not running the washer when PV panels are operating; data monitoring to inform occupants of the cause of mould within homes. | “If [social housing landlords] have that data to hand, they can just say—Look you put your fan on once in the past month and this room hasn’t been heated at all and that’s why you’ve got all the black mould”. P6 | 4 | 5 |
H. Models are too expensive to purchase (speculative) | Participants discuss the high cost of purchasing models, preventing better models being used more widely. | E.g., when trying to purchase models for large amounts of data, BIM, particularly Revit. | “[Models] are pretty robust but they are quite expensive, so how do you get the sweet spot between something being robust and people being able to take decisions on the basis of them, at cost”. P2 | 3 | 3 |
I. Occupants are excluded in data monitoring | Participants do not monitor or retrofit buildings with occupants in them due to an increase in complexity. | E.g., co-heating tests involve heating a home to 25 degrees which is assumed as unacceptable for occupants to live through; large sample sizes needed to account for vast variation in occupant behaviours and add difficulties in monitoring occupied homes. | “Because there are so many occupant angles that affect energy use, it becomes impossible [so] we are all focusing purely on building fabric”. P4 | 2 | 3 |
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Coulentianos, M.J.; Abbey, D.; So, C.T.; Ward, W.O.C. An Expert View on Data and Modelling for Planning Domestic Retrofit. Buildings 2024, 14, 887. https://doi.org/10.3390/buildings14040887
Coulentianos MJ, Abbey D, So CT, Ward WOC. An Expert View on Data and Modelling for Planning Domestic Retrofit. Buildings. 2024; 14(4):887. https://doi.org/10.3390/buildings14040887
Chicago/Turabian StyleCoulentianos, Marianna J., Danielle Abbey, Christy Tsz So, and Wil O. C. Ward. 2024. "An Expert View on Data and Modelling for Planning Domestic Retrofit" Buildings 14, no. 4: 887. https://doi.org/10.3390/buildings14040887