Managing Ventilation with CO2 Monitoring

In 2003, a healthcare worker infected with SARS went to a wedding in Hong Kong and checked into the Metropole Hotel. He fell ill the next day and went to hospital – but had already infected 16 other guests with rooms on the same floor, probably largely through their ventilation systems.

Move forward 17 years and managing ventilation is now a hot topic in reducing COVID risk. A person with COVID releases particles in droplets and aerosols whenever they speak, sneeze or cough. Droplets tend to fall to ground quickly, but finer sprays (aerosols) remain suspended in the air for long periods of time if the air is not removed from the room. Breathing in these aerosols puts other people in the room at high risk of developing a COVID infection, even once the infected person has left.

Fighting this means bringing in fresh air and removing stale air to quickly remove virus particles from the environment. It is an important piece of any plan to reduce COVID risks in a public building, along with keeping distance, hand hygiene and mask wearing, but it can be difficult to monitor how effective ventilation strategies are. CO2 monitoring can give reassurance that ventilation is working to help you get your building back to work safely.

How monitoring CO2 helps

CO2 levels provide a very useful signal for any facility manager trying to assess COVID risk and ventilation rates. Every person in a space breathes out CO2 at a constant rate, at a concentration around 40,000 parts per million (ppm) meaning it can give a good indication of aerosol load in the air, if someone in the room has COVID.

Over time this CO2 builds up in enclosed spaces, so that levels will exceed the natural background rate (around 400 ppm). An effective ventilation system will be able to expel stale air and bring in fresh at a rate that keeps the CO2 levels from building up to uncomfortable levels: and by diluting any infected air it will also reduce the risk of contagion to the people in the space.

Understanding CO2 levels across the whole building helps facilities managers identify what strategies are working, reduce high risk practises (such as holding meetings in unventilated spaces) and demonstrate to occupiers that the space is working for them in the fight against COVID.

What should CO2 levels be?

If you’re thinking about using CO2 to control COVID risks, it important to recognise that while absolute levels do matter, rate of change is important as well. A rapid build up of CO2 in a room is a clear sign of over occupation, and a slow reduction can be a sign of ineffective ventilation.

Background (atmospheric) CO2 levels vary slightly over time and location but are typically around 400 -420 ppm; in a mechanically ventilated buildings 800ppm – 1000ppm is a commonly used target for normal operation. Even before COVID, research shows levels above 1200 ppm have measurable impact on productivity and decision making (see CO2 and workplace productivity below).

The chances of catching COVID are affected by a range of factors as well as ventilation (including age, activity level and distance), and these effects are still being researched, so providing a single CO2 level as a target is misleading, but current consensus amongst authorities such as ASHRAE and the FEA suggests anything above 1000ppm should be avoided.

Prof Cath Noakes, expert in ventilation and infection transmission, goes a little further: “You should be looking for CO2 below 1000ppm, and ideally around 800ppm BUT there’s a bit more to it…to understand if the ventilation is adequate, you need to measure with the normal number of people in the space. If you measure with less people you will get a lower reading which could give a false impression that the ventilation is OK.”

What else can CO2 levels tell me?

As important as the absolute levels of CO2 is the rate of change. As Prof Noakes explains: “Transient effects also matter. The CO2 builds up and decays quickly when a room has a high airflow rate, but much more slowly in a poorly ventilated space. A single low reading doesn’t tell you the whole picture.”

Watching the rate of change gives a clear picture of which parts of the building are extracting waste air effectively. The graph below shows the decay of CO2 in a meeting room with good ventilation, vs ineffective ventilation.

CO2 decay in well ventilated (left) vs poorly ventilated (right) meeting rooms

Using decay curves like this it is possible to produce an estimation of air changes per hour and map these over the whole building to identify dead areas where ventilation is less effective. Sometimes there can also be ‘dead times’ – in one building monitored by Purrmetrix system last year meeting rooms that were effectively ventilated during operating hours were being used after working hours when ventilation systems were reduced. CO2 build up was rapid and dispersal very slow. The monitoring highlighted this risky behaviour which was discontinued.

CO2 and productivity

Improvements in ventilation have value beyond reducing COVID risk – many studies have demonstrated the link between high CO2 levels and reduced productivity in office workers. Most recently the Whole Life Performance study, from Oxford Brookes and LCMB, used Purrmetrix CO2 monitoring to demonstrate the relationship; finding subjects completed sample tasks 60% faster in environments with lower CO2 levels.

Decline in performance across a range of thinking tasks when exposed to high levels of CO2

In schools, high CO2 levels also a cause for concern, and have been associated with declines in cognitive function scores in at least one Harvard study.

How do I put CO2 monitoring in place?

With all this in mind there are several key things to think about when designing a CO2 monitoring project:

How accurate are my CO2 sensors – how do they calibrate?

Before getting stuck on accuracy, it’s important to understand when it’s important and when it isn’t. Sensor manufacturers list accuracy as ± ppm ± a percentage of the reading – it’s common to find variation of 50 ppm and 3%, meaning at the lower limit of 400 a sensor can measure between 358 and 442 ppm. By sampling rapidly (up to 20 times a second) and taking an average they improve the accuracy of the read and produce a result close to the actual target. Accuracy measured this way will improve at higher CO2 levels (measuring 1000ppm the same error could produce a result of 920 – 1080 ppm, before averaging) and it has little effect on rate of change measurements.
As important as accuracy for long term monitoring is how the devices are calibrated. Low cost devices can drift in performance over time, and manual recalibration is very labour intensive. The best solutions have sensors that can self calibrate and reset back to a base level of background CO2 on a regular cycle.

How easy is it to deploy and maintain the sensors.

Sensors need to be robust, easy to fit, and – if you are working with a large site – easy to identify. Once fitted, the best can be left with no further visits for calibration or battery replacement.

Where am I putting the CO2 sensors?

This is important as you want to get the most representative figure for a gas that will vary across the space. Measurements should be taken from every room where people regularly gather and ideally from the same number of points across an open plan office as points where the air is extracted. The best locations are fairly central, not too low (CO2 sinks so this will raise your result), not directly in front of a person or a vent. We favour underside of desks or meeting rooms, if power leads will extend to those locations.

How much data do I get? How do I make sense of it?

For professional ventilation management, transient effects matter, meaning it is important to be able to see data over long periods of time, and ideally understand quickly which area this data relates to. On a large site all this data must be quickly turned into actionable information, such as heatmaps, alerts and ventilation measurement.

As facilities managers plan for re-opening sites, ventilation provides an effective way to reduce COVID risk and CO2 monitoring is an important tool in managing ventilation strategies. With hundreds of CO2 sensors monitoring thousands of hours of CO2 data, the Purrmetrix solution is a proven and powerful system for measuring and analysing ventilation rates. If you are working on ventilation strategies in your estate and have questions, get in touch and we can help.

Energy efficiency in housing – the business benefits

The benefits of energy efficiency in housing are, quite rightly, often framed in terms of the impact on climate change and the lives of tenants. But this can mean landlords overlook the fact that there is ample evidence that more energy efficient homes repay the investment needed to upgrade them.

Benefits come from a range of cost centres, and data for some of these is not commonly held by many social housing landlords. So we have assembled some of the most relevant studies and data as a brief reference to set expectations about what can be achieved from improving the energy efficiency in housing.

The largest data set available on the impact of energy efficiency comes from a Rockwool survey of social housing landlords, which provides some striking findings:

  • a clear relationship between energy efficiency and number/duration of void periods.
  • properties with lower energy efficiency generated more rent arrears. An average of two weeks more arrears for a home rated F compared to a home rated C.
  • for every 10 point improvement in the SAP score a reduction in average annual costs of responsive repairs of £90
Month of tenant rent arrears per SAP rating

Additional work done for Orbit Housing identifies an opportunity to reduce customer contacts by 75000 over 20 years if their 27,000 homes are improved.

What you need to know before you start condensation monitoring (with examples!)

It looks like a bumper year for condensation claims. As the second lock down and social restrictions increase the number of hours families spend at home, humidity levels in housing are soaring.

As any school kid can tell you, human beings are 60% water, and the spaces we occupy have to be able to dispose of the water we give off. Getting this done effectively is helped by a warm environment, but the COVID crisis means many tenants are finding their incomes reduced, putting pressure on their budgets for heating. Colder homes, occupied for longer, are a recipe for condensation and mould growth.

Condensation is a classic example of the sort of problem that if caught early and treated correctly will cost a lot less than if left undetected. It is also the problem that most frequently causes a breakdown in landlord tenant relationships, as tenant’s behaviour is often a significant contributing factor. And no-one takes kindly to being told they are part of the problem. So early detection of a condensation problem, before mould gets into the fabric, is important to trigger an action plan and keep everyone happy.

Many RSLs have been looking at pilot projects based on RH measurements to help pick up on early warning signs of condensation. If you’re in this situation, we’ve dug back through our data to give you a short guide on what you need to look for to make a success of condensation monitoring. What is best practise to get the most accurate results? What are the metrics you might look for? What can you do with this data?

Some basic physics

Wikipedia tells us that relative humidity (RH) is the ratio of the partial pressure of water vapor to the equilibrium vapor pressure of water at a given temperature. Well, thanks Wikipedia. 

In practise what this means is that RH is an expression of the air’s capacity to hold water vapour at a given temperature. Air can hold a lot more water vapour at a high temperature than at a low temperature, which is why we end up with water condensing when warm air hits a cold surface.

This means you can have a lot less water floating around in the air of a room that is 17°c with an RH of 70% than in a room which is 22°c with an RH of 50%. So the first thing to note is that simple RH %ages can be a bit misleading when it comes to measuring condensation risk.

If you take your 24°c room and keep the same amount of water, then as you reduce the temperature the RH will rise, and the point at which it becomes 100% (ie the point at which condensation occurs) is the dew point. In this case, with 50% humidity the dew point would be about 11°c.

The third metric worth knowing is the actual vapour density, that is the weight of water in the air. If you can get it, this is one of the most useful metrics to watch in condensation analysis because it’s the measure of how much water occupants are putting into the home.

RH is not the only game in town.

We’re focussing on the physics here because it is our belief that many landlords are missing valuable information by focussing on RH alone. Instead, running analysis on dew point and weight of water uncovers information that is richer and more accurate, allowing interventions to be better targeted. For example, here is a side by side comparison of two homes both of which have a serious RH problem, with measurements regularly in the 80% plus zone:

Take that data and turn it into dew point and you can really see which house has the problem. Here the black line is the real temperature, and the blue line is the dew point (when condensation occurs). House A is spending significantly more time at or below dew point, and is undoubtedly wringing wet. The other house has occasional incidents.

How to gather effective condensation data.

For any monitoring project two key questions are: where do you measure and how long do you measure.

I probably don’t need to spell this out, but if you’re looking for condensation problems using RH data, take measurements where you expect problems to appear. External walls, close to corners, in heavily occupied rooms are generally a good bet. 

However, to gather enough data to think about all the root causes, you are very likely to need more than one sensor. Measuring conditions in bathroom and kitchen will help confirm if ventilation is working correctly, a sensor closer to the most used heater will confirm patterns of heating use. 

Bear in mind that you need to look at the behaviour of the home with a variety of weather conditions and occupant behaviours – so plan for 3-4 weeks of monitoring.

How do I make sense of all this data?

If you’ve confirmed using dew point analysis that you’ve got a serious condensation problem, the obvious next question is what is causing that?

Condensation problems are generally the result of a combination of problems. To get condensation you need 1) a source of water vapour (hello humans!) 2) a cold surface and 3) air that isn’t able to circulate effectively to remove the water vapour, either because it is too cold or because it’s not being removed from the building.

Most condensation problems can be placed somewhere on this space:

Deciding where you are on this triangle will help define the action you need to take. By looking at weight of water in the house and mapping it over a week’s use it is easy to pick up the contribution from lifestyle.

For example here we have footprints from two homes – each row is 24 hours of data. One house has a significant high base level of humidity and the other has a pattern that clearly shows morning showers. Of course, neither of these are a problem in themselves, unless the dew point vs temperature analysis shows high risk of condensation.

More heating is often mentioned as a solution to condensation. This works by 1) raising the temperature of the cold surfaces in the home and 2) allowing the air to hold more water vapour so it can be removed from the home better, if the ventilation is working.

Condensation is commonest on external walls, so it’s important to consider, before asking tenants to run their heating for longer, whether the fabric of the home is losing heat too fast. A home with walls or ceilings cold from heatloss will be very difficult to heat sufficiently to avoid condensation (as an easy example, it’s nearly impossible to heat a bedroom sufficiently to avoid condensation on single glazed windows on a cold morning). We will be writing more on how to gather information on heatloss shortly, so keep checking back in.

If heating is adequate to mobilise the water vapour then the final part of the jigsaw to look at is ventilation. The simplest approach is to look at the time taken to reduce water vapour to normal levels after an event like a shower. The chart below gives a few examples of ‘natural’ and ‘artificial’ ventilation in an older house, showing what happens after cooking and showering.

Analysed correctly, RH readings are a rich source of information that can not only confirm the extent of condensation, but also predict where problems are likely to occur and demonstrate how to tackle them. Purrmetrix provides easy to use, powerful tools for measuring and analysing condensation and RH problems in any home – if you have a problem with condensation that would benefit from diagnostic monitoring, contact us for a demo or more information.

If you can’t measure it you can’t manage it

For some people there is only one metric that matters when thinking about energy in their buildings – the bill. And the only outcome that is interesting is driving it down.

We’ve all lived with people like this. Sometimes we have argued with them over the setting for the thermostat. Or even found ourselves sitting in the dark as a light automatically switches off.

These incidents make an important point about energy efficiency: one that we know intuitively. A simple focus on the input (the energy bill) can lead to poor decisions on how a building is used. At its most extreme it can undermine the purpose of a building – to shelter its occupants and foster productivity – completely.

A more fruitful way to think about building energy efficiency is to understand what outputs we want from a building for the energy we put in. Although this is harder to do, it results in buildings that are both truly efficient and more productive for those working in them.

So if building energy efficiency means using less energy to provide the same service, how can we measure this? And what can it tell us?

Measuring true efficiency


To benchmark true efficiency you need to find metrics that can be used across a wide range of very different buildings. So it is standard in energy efficiency to talk about energy consumption per floor area and energy consumption becomes KWh/m2.

This helps with comparisons between buildings but tells you very little about why some use more energy than others – are their lights on for longer? Do they need more heating energy because they are draughty? Measuring outputs not only gives a more accurate picture of energy efficiency but it can also help identify where waste happens.

Where energy is used

Energy in buildings is turned into a lot of outputs and a complete accounting of these is impractical. How can you compare output used to charge a phone against the load required to boil a kettle?

However, the majority of energy used in commercial and domestic buildings goes on two significant and measurable outputs – keeping comfortable air quality (temperature, humidity and ventilation) and keeping the lights on. These two end uses will typically account for nearly 70% of the energy footprint in offices and homes, so if they can measured and compared, a much clearer idea of the relative energy efficiency can be produced than relying on bills alone.

In practise, this can be turned into a measurement of Kwh/m2 of usable space, where ‘usable space’ is space that meets requirements for air quality and lighting.

How Purrmetrix can help

This philosophy is already reflected in processes such as PAS2035 and post occupancy evaluations, where monitoring and verification are used to confirm that energy efficient buildings are delivering the outputs they are meant to.

For property professionals who are delivering such evaluations, our mission is to make it easy to collect the data you need on building performance and turn it into useful information.

With sensors for temperature, humidity, lighting and CO2 and a sophisticated analysis platform the data can be turned into simple metrics of building output, so you can explore how investments in energy are paying off.

Accurate and detailed data on building performance lets you go beyond benchmarking and identify opportunities for reducing energy through optimising building controls and improving building fabric.

And measuring performance before and after improvements provides evidence on what works – and what doesn’t.

Get in touch if you would like a peek at what can be done with environmental data.

Decarbonising housing stock with data

With 26 million homes to upgrade by 2035 to meet the Government’s clean growth strategy, the UK faces a gargantuan task. Delivering on this date requires a home to be upgraded every two minutes, and significant upgrades in processes for assessing, procuring and managing retrofit projects.

In response, a new generation of solutions are emerging that help landlords improve ROI from their energy strategies. In this article for Housing Technology we look at how Social Housing Landlords are meeting the challenges of decarbonisation and some of the most exciting solutions being used.

READ MORE HERE

3 great business cases for IoT in older buildings

Every day I speak to a lot of property professionals – FMs, building engineers, asset managers. There is a lot of interest in turning existing buildings into smart buildings and using the IoT to improve the performance of buildings but still people are stuck with the business case. Clients won’t invest in the tech unless they can see a clear ROI; property assets are so variable that guaranteeing ROI is tricky.

Frustrating for property professionals who can see big efficiency gains in their operation if they can get better data from their buildings, but who are stuck without the tools to persuade clients to invest. Frustrating for clients who are hearing a great deal about the promise of IoT but can’t relate its benefits to immediate pain points.

After three years of pilots, turning average buildings into smart buildings, we have a large estate of sensors in operation, so we’d like to offer three clear business cases to discuss with clients. Tread carefully: different clients with different types of tenure will respond better to some than others.

1. Energy savings.

Want to save 10% of your gas bill simply by changing a setting in the BMS? We’ve written about energy savings before at length but the simple message is that nearly every older building will have opportunities for saving energy at *no* cost beyond identifying where controls don’t match the operations of the building. A couple of examples:

– the call centre in North West England which saved 18k on energy by changing HVAC set points and reducing conflict between zones

– the airfield in Eastern England who identified nearly 100k of wasted gas spend from poorly controlled heating in their hangars

The business case. Most older buildings waste energy simply because the control of their heating and cooling no longer matches the operation of the business. Timings may be off, unused areas may be heated, heating and cooling may be in conflict with each other. These issues are extremely easy and cheap to fix, and lead to rapid payback, but first they must be spotted. A properly smart building uses simple sensors to confirm that the BMS is doing exactly what it should be.

To calculate the possible ROI here are a few basic rules of thumb:

Costs of heating (or cooling) 1 m2 each operating hour £3.60 (from CIBSE benchmarks). Put another way, for a typical office operating a 9 hour day the annualised cost of 1 hours’ HVAC psm is £32.40. Therefore in a typical 1000 sq m office reducing the heating run time by an hour would save £3,240 on your annual heating bill.

Reducing set points by 1° can produce savings varying from 3 – 7%. So for the typical 1000 sq m office above, with an annual energy bill for HVAC of £29,160, that would be an additional £874 – £2041. These figures are hugely simplified – in reality the results will be complicated by factors including external temperature, and varying amounts of savings depending on how far your building is from the external temperature.

The faster and more frequently you can find these energy savings opportunities the more money you can save – at a minimum an annual audit (not from BMS data) should give you substantial payback. Remember these are *no cost* fixes.

This is a business case that appeals most to owner occupiers of space who have some control over their building services and who are invested in reducing their energy costs.

2. Space utilisation

Fun fact: fully loaded with rent, rates, utilities etc, desks in the UK cost employers between £3000 and £15,000 each year. Yet a lot of them are empty for a lot of the time, with utilisation rates often below 60%; so if ever there was a business case for better building data it’s in space utilisation.

So if your client has a large and fluctuating workforce and a big estate then they are probably already thinking about better space utilisation and if they’re not, they should be. Although the cost reduction argument is strong its not the only one, as it is possible both to reduce space and to provide more of the most heavily used facilities so that workers are better equipped and feel supported.  The range of new options for flexible working, from providers like The Office Group and WeWork, and a huge range of digital team working tools, mean that short term staff fluctuations can be easily accommodated and folded into the organisation.

For a quick business case on utilisation audits to discuss with your colleagues:

Estimate fully loaded desk costs (calculator by region here), multiply by number of desks and then ask what level of underutilisation you would need to find to justify the audit.  Even with audit costs of £100 – £150/desk, you’ll find it’s surprisingly small.

3. Productivity improvements

This is where the big wins are. Staff costs are by far the largest cost component of most businesses, and even small improvements in staff productivity can be game changing. It’s not just call centre operatives able to handle 10% more calls each day or traders making 2% more trades but also improvements in the quality of work from creative teams, or better focus from staff tasked with safety critical jobs.

Frustratingly, its also the hardest to prove. Here’s a summary of what we do know:

– Many studies have shown that indoor environmental quality has a measurable impact on people’s productivity

– Poor temperature control can reduce productivity by up to 10% at +/- 5% around a ‘safe’ band

– Better lighting can raise productivity by 23%

– Better ventilation increases productivity by 11% (link to UCL work)

– Productivity in different tasks can be affected by different aspects of the environment (for example noise levels)

The ROI from a better working environment goes beyond enabling staff to concentrate better. A better building reduces absenteeism and staff churn, so that recruitment costs drop.

Not all of these business cases will work for every client, but nearly every client is interested in saving costs and building a stronger team. The next time your client asks you how you can do something different in tired old buildings these are the conversations to have.

Beyond the data: controlling CO2 with Purrmetrix

This is cool: the team at LCMB have been using Purrmetrix to go beyond data gathering and get into some home brewed building control.

LCMB have been monitoring the environment in a university facility for several months using Purrmetrix CO2 and temperature sensors to look at the relationship between comfort and productivity. One space in particular caught the attention of LCMB and the university estates team: an office area which had obvious ventilation problems. With a lot of people using the space the building systems were struggling to keep up with the ventilation needed, especially in winter, when opening windows was unattractive.

It was commonplace to see CO2 levels rising above 1500 ppm, a level where concentration and decision making becomes impaired.

KCL CO2

But rather than going for a costly building services overhaul, the LCMB team decided to go one better and implement their own fix, using ducted fans and a Raspberry Pi to keep the CO2 in the office within controlled limits.

Two fans were set up to provide good coverage across the whole space; each had their own Raspberry Pi low cost computer to read the data from API on the Purrmetrix web service and to drive the fans using a digital to analogue converter. The fans were ducted from outside so the set up also needed a heater in front of the air to temper it to the right temperature. This might not be the prettiest set up, but it’s a prototype and a big improvement on a CO2 headache.

KCL fan

The project took a few days of tinkering time to connect the hardware, in particular setting up the Pi and configuring the internet access for it from the university network. But once the hardware was connected and the right scripts were implemented it produced an immediate result.

KCL CO2 controlled

There had been some concerns about noise levels and air movement – in practice the team found that to keep levels of CO2 around 700 ppm (+/- 100 ppm) the fan was operating at 50 – 60% for much of the day. This delivered a good trade off between air quality and noise level. Allowing a higher CO2 level (around 1000 – 1200 ppm, which is considered acceptable) fans could be quieter, cycling on/off most of the day. In warmer weather as windows are opened, the fans are working much less frequently.

‘Once we had the data from Purrmetrix it was irresistible to have a go at creating a solution.’ explains Tom Cudmore, senior consultant at LCMB, ‘We considered using the alerts system within Purrmetrix to create a HTTP call that would activate the fan, but in the end we decided to keep the alerts for exceptions and take the data direct from the API every two minutes. It took a few weeks to source the right fan product and write the scripts, but we think the results speak for themselves – we’d be happy to work in this environment now.’

If you have a problem office and would like to re-create this low cost solution, to trial the impact in your workplaces, here are the components you will need:

– Fan (we used Helios RR-EC in-line duct fan) sized according to the room volume and desired conditions
– Ducting and window blanking plate
– Heater to put in front of the fan and temper the outside air
– Power supply for fan and heater
– Wiring from analogue convertor to fan
– Digital to analogue convertor – we used a LucidControl device
– Raspberry Pi with internet access and configured for remote access
– Purrmetrix kit – in this case with CO2 sensor

And if you want a copy of the script that LCMB developed, just get in touch.

When good buildings go bad…HVAC recommissioning

Proptech. AI. IOT. Space as a service. Smart Buildings.

There’s increasing noise in the property industry about the importance of technology. You’re probably sick of the phrase ‘Data is the new oil’, especially if you’re tasked with trying to persuade a sceptical boss to invest in it.

Not surprisingly we have a lot of conversations about the ROI from environmental data, which can get complicated as there are many ways in which better insights into your environment create value. So we thought we might create a short series where we could share examples of what our customers are doing with this data and how they get ROI.

First up – how to reduce hvac energy waste

Fact: the energy consumption of commercial buildings tends to drift up over time. If you look at the lifecycle of a typical modern building over time the energy used to run this building will tend to trend upwards unless regular interventions – shown by the green line – are made to review and fix the problems that arise.The effect of control drift

Why these occur is complex, but one of the most common and low cost causes is control drift, and that’s what we’re interested in. Studies in the US of over 600 commercial buildings show more than 50% have controls problems and that fixing these produces a simple payback within a year. These are very simple issues like scheduling, run time, set point adjustments, eliminating zone conflicts.

Further more, fixing them – recommissioning the building – is generally a very low cost win, requiring no new investment. So this looks like an attractive target for improving energy efficiency in any building, but to do this first you have to find the problems.

Causes of control drift

So it’s a common problem, it is low cost to fix – why don’t people pay more attention to it?

I think part of the problem is that, unlike lighting, HVAC control issues are invisible – the BMS is unlikely to flag it up as an alert, and identifying it requires expertise and time. If your buildings supports multiple schedules and set points – and we have come across academic set ups where there are hundreds of schedules for heating and cooling – then manual checking isn’t practical.

Furthermore many facilities managers are a bit sceptical of data reported from a BMS because they know sensors can drift without recalibration and they are often located away from the occupants. If managers don’t trust the data the BMS is producing, then they are less likely to invest the time in an audit.

The value of environmental data in these situations is that, unlike metering data, it is easy to locate exactly which part of the system is running out of control and implement the right adjustments.

The case for recommissioning

A quick back of the envelope highlights why this is worth doing – heating and cooling commercial space is typically 25 – 35% of the overall energy budget for a building. That means, at its simplest, that if the building systems are running for 10 hours where they could be running for 9, that extra hour is 2-3% of the energy budget for the whole building. Running at weekends is ruinous and not uncommon. In fact, based on experience, I’d go so far as to say if you have a building of more than 3 years old, its a stone cold certainty that parts of the HVAC will not be well matched to the operations in the building.

Fixing control drift – real world cases

Over the past two years we’ve seen so many examples of this, and not just in offices:

1) the 10 year old office building in a campus setting with conflicting HVAC zones and heating that was regularly 2 or 3 degrees above set point. Changes to reduce zone conflict and lower temperatures produced a 20% reduction on an annual 80k energy bill

2) a school with a large number of Victorian school houses and a wet radiator system that had extensive challenges with timing heating, incorrect sensor readings and furniture in front of radiators. Fixing these problems produced a 10% reduction in their gas consumption for these buildings.

3) an aircraft hangar with substantial overheating issues and heating systems running out of working hours. Purrmetrix data demonstrates that upgrading controls will create savings of over £100,000 across the whole estate.

Think you might have a silent controls problem? Get in touch  and let’s talk about how we can help.

Does your building need a data strategy?

How much data is your building generating? Are you getting any value from it?

Even if you are not operating state of the art smart buildings with nearly 30,000 sensors, the chances are that you will soon be swimming in data. Industry estimates suggest that we’re well past 500 million end points from commercial buildings, and heading towards 3.5 billion in the next three years (thanks, Gartner). That’s end points…each one delivering a data stream at rates potentially of many times a minute.

data endpoints in buildings

Its a crazy amount of data, and it won’t be tractable without some deep thinking on applications and analytics. If properly harnessed it offers huge scope to improve the efficiency of buildings and their capacity to deliver comfort and improve productivity for the people working in them. For that to happen, though, building operators need to get a bit cleverer about their data and clearer about what their data strategy needs to be.

Where to start with data in your building

A good data strategy will clarify the value of the data being collected and ensure it is fit for purpose – sadly not the case in so many buildings right now. A simple example – consider the difference between a supply air temperature sensor 3M up in an office ceiling and a desk level comfort sensor. This difference is not just location, but also the rate of update and data availability for analysing previous periods. Lower data rates may be good for control applications, but there are certain applications that are only possible with higher data rates.

Screen Shot 2017-11-24 at 19.51.48

So data strategy should help flush out what you want from your data, which drives some important decisions about what, where and how frequently to measure.

The best – and simplest – first step is to assess what your goals are for data. What are the really valuable applications? For most commercial buildings the two major categories of time related data – metering data and building management systems – have obvious lead applications – settling energy bills and controlling building services, but they typically need some enhancement to be able to support detailed building analytics.

Data goals for buildings

I’ve been around in the smart building market for some years, and as it overlaps with green building objectives a number of clear applications are beginning to emerge that support the efficiency of buildings and the comfort of occupiers: data in green buildings

  • Optimise energy performance – Match energy usage to the buildings activity
  • Additional (retro) commissioning – Providing a regular review of the buildings systems to check they are working as they should
  • Measurement and verification – Demonstrating that improvements are performing as anticipated
  • CO2 reporting – For external and internal communications
  • Permanent environmental monitoring – Ensuring energy efficiency does not compromise building’s delivery of comfort. Identifying wasted space and opportunities for supporting improved productivity
  • System innovation in design – Identifying new opportunities for energy savings and improved productivity by making major changes to environment.

From green to brown – how value of data can trickle down

The evidence is that these applications add real value to buildings – green buildings are typically 25 – 35% lower to operate and occupiers report 4% more productivity from staff. Excitingly, it seems there is potential to apply some of these to buildings that haven’t originally been conceived as green or smart, to improve them.

As a quick, scaleable win for occupiers and operators of commercial, data has huge potential, if it is structured correctly. Purrmetrix can help fill in gaps and support your analytics – if you want to know more, do get in touch.

New: movement sensors and sensitivity – the ‘Mission Impossible’ test

How easy is it to fool a movement sensor?

I can’t be the only person who has been working late at night in an office when suddenly I am left sitting in darkness, because the PIRs (passive infrared) movement sensor that controls the lighting has decided I’m too still.

In fact I know I’m not the only person, because an FAQ from customers who want to use our new movement sensors for measuring office space usage is how sensitive they are. And so we decided to do a little experiment in our office with two sensors, some software engineers and a very bouncy visitor.

We wanted to look at a range of activity levels and locations for sensors, including

  • distinguishing between one very active person (moving furniture around), several people talking and interacting and the classic ‘stationary deep thought’ pose beloved of software engineers
  • looking at the effect of location, in particular the difference between ceiling and wall mounted sensors. The ceiling mounted sensor was 2.7m off the ground in the middle of the workspace, and the wall mounted sensor was 1.5m off the ground on a column in a direction that covered half the office space.

Over a week, we found the ceiling sensor accurately reported all presence in the office. It is difficult to distinguish short, vigorous bursts of activity from one person – they look very like a few people interacting – but they did typically tend to be shorter in duration.

Meanwhile the wall mounted sensor did exactly what was expected and recorded all activity in the half of the office it could see. In the analysis below it is the light green trace:

Screen Shot 2017-11-14 at 16.13.53

Most usefully, our tests reveal it’s very unusual for anyone working at a desk to go for many minutes without some small adjustments of their body or working environment. Perhaps reaching for a pen, scratching your nose, or moving a mouse. It’s perfectly possible for someone who is focussed on their work (take a bow my software colleagues) to record no movement for a minute or more but if data is aggregated in 5 minute or larger buckets then we had a close fit between occupancy and movement.

To avoid over sensitivity, our movement sensors work with a target concept that is about the size of a human limb, so, if you are in Tom Cruise mode the best way not to trigger the sensor may be to practise moving one finger at a time at less than 1 m/minute. You might not get any work done, but that’s OK…no one will know you’re at work.

If you want a slightly more technical explanation of the sensitivity and range of our movement sensors you can find the data sheet here.