Month: May 2022

Getting more from your data.

Everyone wants data from buildings these days. Most people don’t want to spend all day (or even an afternoon) trying to make sense of it. 

With that in mind we built the Purrmetrix account to provide tools for quickly making sense of your environmental data and processes to automate the results. Tools in account can be used to produce simple performance metrics, or they can help visualise data for a deeper dive into a building’s performance to understand where things might be going wrong.

With our account overhaul nearly complete, now seems like a good time to revisit some of the most popular tools and give some real world examples of how they can be used.

Building Performance Metrics

Heat loss – measuring HTC for retrofit

A lot of our work at Purrmetrix is digesting a lot of data to deliver single figures of performance. We looked at condensation risk and ventilation rate in our last blog post – another very important attribute for building performance is heat loss, and we deliver HTC SMETER calculations for housing, with our partners City Science and Build Test Solutions. In situ heat loss testing is extremely helpful to cut through the confusion when trying to manage retrofit projects: it helps identify the right homes for funding and improvements and validate that these projects are successful. 

An accurate heat loss figure is the critical building block for determining the heating thermal energy requirement (kWh/m3). It helps refine EPC and archetype data to better understand the real world performance of homes and to inform design decisions. We use smart or conventional meter reads combined with temperature data to deliver HTC reports for each home. This is a delivered as a standalone report at present, with plans to deliver HTC in account in future versions.

Goldilocks – a universal building performance benchmark

For benchmarking performance, Goldilocks sets the targets for performance and then tells you how much time has been spent in compliance. It’s a handy way to compare large numbers of buildings or to compare performance across time.

There are lots of uses, depending on the environmental metric you are using. For example in housing it can be to compare homes to see which homes are persistently cold, identifying possible fuel poverty issues.

It can also be used as a rough guide to homes at risk of condensation issues. In offices or schools it can be used to compare CO2 in meeting rooms or classrooms to show where extra ventilation is needed.  

While we’re talking about general tools, there are also views to calculate comfort metrics, averages and max min for any data stream. These are most typically used when benchmarking performance and comparing it over time, before and after improvements to a space.

Visualisations – making sense of building complexity

Sometimes it’s important for technical specialists to get deeper into the data and see what clues it can give about problems in buildings. For these situations, visualisations can be incredibly powerful, making it easier to digest and make sense of a lot of data. Graphing and bar charts are a standard way of visualising data, and we provide these, but other views go deeper:

Heatmap

Our heat map tool allows the data to be accurately positioned on a building plan and replayed to examine the behaviour over time – can you see solar gain moving around an office building? Is high humidity in the kitchen also affecting the living room?

Waterfall

Each line of a waterfall visualisation heat maps a metric across a complete day. This is a fantastic visualisation for spotting recurring patterns of behaviour; whether it is identifying if heating systems are turning on and off at the right time, or finding a damp problem that is related to external rain events.

Reporting and alerts

All of these tools can be wrapped up in our reporting function, which delivers reports via email on a regular cycle. Of course we also have live data, so if there are situations where a timely response is important then our alerting tools can be used to deliver a call to action. We’ll look at how to use these tools in more detail in a future blog, or you can visit our support section now.

Getting value from environmental data

Recently we’ve been upgrading the Purrmetrix web service to integrate new analytics and upgrade some of the features that make it easier to work with your data. 

This blog is about introducing a couple of new analytics – on ventilation rate and condensation risk – both of which make sense of a lot of data from environmental sensors to deliver a metric that can drive action.

(Later in the week we’ll also take the opportunity to remind you of some old friends in the analytics menu – one thing that we have learnt from chatting to users is that heat maps and graph views are doing a lot of heavy lifting in our account. We love graphs and heat maps, but if you’re investigating a problem building, or trying to benchmark performance, there are quicker and simpler ways to interpret Purrmetrix data using some of our other views.) 

For now though let’s talk about new analytics in account and what they mean for anyone in charge of a commercial, educational or residential estate.

Ventilation rate

COVID has taught us that ventilation indoors is crucial for our health; as a result CO2 monitoring in schools and workplaces is now becoming popular. But absolute CO2 numbers only tell you part of the picture – a high CO2 number may be a temporary result of too many people in a space. Or it may be a small number of people in a space which has very bad ventilation. The difference between the two is in how fast stale air is removed, and this is what our new ventilation rate analysis looks at.

By finding the data where the CO2 in a room is declining sharply we can make measurements of how quickly the air in a room is removed. This is an analysis that works particularly well for smaller, well populated rooms – living rooms in housing, meeting rooms or classrooms / lecture theatres, as well as break out zones, smaller offices or cafes. All we need are some people who will stay in the space long enough to breath out some CO2 and then leave.

The ventilation rate calculation takes a whole bunch of CO2 data and turns it into a single metric – an average for ‘air changes per hour’. This allows comparison across different sized rooms so that areas with poor ventilation really stand out. 

Currently our ventilation rate analyses are still in beta, but for any customer who wishes to try out the initial calculation we would be happy to upgrade your account to beta for free. Just get in touch.

Condensation risk

Condensation and its buddy mould growth are an expensive and persistent problem with real impact on health. It’s a tricky problem for landlords for a couple of reasons. Firstly, it’s often a hidden issue: the first time a landlord may be aware of them is a tenant complaint once the problem has escalated to mould and fabric damage. Fixing the issue can then be a lengthy struggle. Secondly, sometimes the causes of condensation are related to tenant behaviour and sometimes to fabric so it’s very easy to apply the wrong fix for the problem.

Identifying homes with hidden condensation problems before it escalates saves time and money on extensive repairs and helps avoid any health problems for tenants. Better yet is identifying ‘at risk’ homes; houses that may not show a significant problem but where a change in circumstances (such as significant changes to fabric, adding to occupancy or reducing heating) could tip the house over into a more serious issue. Because condensation can be a localised problem its often hard to identify from a single point of measurement, and to get a rounded picture of what is going on analysis needs to be done from several points within a home, including high risk areas like bathrooms, kitchens and bedrooms.

Measuring several points in a home can make things complicated but our new condensation risk metric helps to simplify the process and deliver a metric that highlights risk areas and allows them to be benchmarked to track which interventions are working. The risk metric identifies the conditions needed for condensation and mould growth to occur and calculates the portion of time each area is at risk. It then allocates an overall high / medium / low score to the home based on the conditions seen and calls out the area that is worst performing.

For high risk homes, further investigations using other Purrmetrix views can give a good understanding of whether the problem is being driven by behaviour or fabric.

Analytics for housing – what’s next?

These new analytics will be available to all Purrmetrix customers on beta accounts (contact us for access) for the next few weeks, following which we will launch them into the pro level accounts.

We’re always open to talking to customers about how to get value out of environmental data. Whether it’s diagnosis of housing defects, insights into occupant behaviour or forecasts of the impact of retrofit / heat pump installations, we believe data will give clearer insights and help drive better actions. If you have a data challenge, please do get in touch.