A Building Management System (BMS), is a computer-based system integrated within buildings to control and monitor various equipment, notably Heating, Ventilation and Air Conditioning (HVAC), and lighting. Studies have shown that the BMS is responsible for managing between 60-80% of the total energy consumption in a building, making its performance a vital consideration as part of carbon and cost reduction strategies. Some often associate the BMS with a single computer in a management office or control panel in a plant room, however the BMS is better described as a nervous system throughout the building. The computer sometimes seen in buildings is purely an interface known as the ‘head end’.

The BMS itself is typically concerned with the immediate operation of equipment, based on the feedback of the numerous ‘data points’, such as temperature sensors, at that precise point in time. This data is rarely logged, which means that the BMS is incapable of analysing its own performance. This is a task left to specialist engineers who will then rely on the ‘snapshot’ of information the BMS is giving them at that point in time only. As equipment has become more complex and widespread, analysing performance has become an even harder task.

Building Analytics is a smart building technology which continually gathers and analyses data from across a building to identify, measure and ultimately improve overall operational performance. By gathering real-time data from across the various systems and equipment within a building, building analytics provides an accurate evaluation of the building’s health, energy consumption and comfort for its occupiers.

Our building analytics solution at Demand Logic provides actionable intelligence to property managers, facilities managers and building contractors. It is intended to deliver quantifiable benefits in a short space of time, such as identifying opportunities for saving energy, improving maintenance and increasing occupier comfort, while reducing operating costs.

We generate this intelligence by creating a “digital twin” (more formally, a mathematical model) of every single item of equipment that our system detects, whether that be a high-cost asset like a chiller or a smaller item like an individual fan in an air-conditioning unit. There are many thousands of such devices in a typical building. The important thing is that each digital twin knows both what it should be doing and also what it is actually doing. This is how we can make a running assessment of performance of all the equipment, and the system overall, and how we can highlight the needle in the haystack that needs attention.

The term HVAC stands for Heating, Ventilation and Air Conditioning. HVAC systems are responsible for providing comfortable Indoor Air Quality (IAQ).

Usually within commercial buildings, an HVAC system includes the major plant (equipment) which provides the source of heating/cooling e.g. chillers or boilers, as well as the associated pumps, fans, motors, valves which support and enable to transition of heating/cooling around the building whether this is through water, air or refrigerant.

A net zero building, also referred to as Zero Energy Building (ZEB), zero carbon, or net zero means that the total amount of carbon that the building emits on an annual basis is equal to the amount of renewable energy that it creates. Both operational carbon emissions and embodied carbon emissions contribute to annual emissions.

Net zero is often confused with carbon neutral; which actually means that while some emissions are still being generated by a building/process these emissions are being offset somewhere else making the overall net emissions zero. Renewable energy can be achieved through technology such as solar panels, wind panels or heat pumps, using these systems as alternatives to carbon-based energy.

While the road to net zero is a target that the UK is committed to reach by 2050, there is still a lot to be accomplished to reach it.

Operational carbon emissions are currently more easily quantifiable although a tendency for building owners and facilities managers aiming to achieve net zero is to instruct retrofits which likely in turn increase embodied carbon emissions. Optimising the efficiency of existing equipment by identifying the areas within buildings where energy consumption can be reduced is therefore vital.

For identifying these areas, building analytics systems must be applied to gather data from across multiple building points, analyse them and indicate where energy optimisation can be achieved. Building analytics can help reduce carbon emissions, bringing buildings closer to the net zero goals.

There is currently no definitive definition for a smart building and there is a drive, by some, to resolve this. Broadly speaking however there seems to be some agreement that a smart building should be a net zero building and function with the end user in mind. It is expected that a smart building will utilise multiple technological systems and devices, including IoT (Internet of Things), to share data about its performance as well as building analytics.

Devices can be installed to track data from across multiple points, such as: Indoor Air Quality (IAQ), Indoor Environmental Quality (IEQ), energy consumption, water consumption, carbon emissions, occupier comfort and much more.

Indoor Air Quality (IAQ) refers to the quality of air within and around buildings and structures, related to occupiers’ health, comfort, wellbeing and productivity whereas Indoor Environmental Quality (IEQ) has a wider scope, looking at other metrics such as sound, light and smell as well as IAQ.

Indoor Air Quality (IAQ)

In commercial buildings, IAQ improvement is being seen as a way to improve conditions for employees, especially in offices, and even to improve customer experience, in retail. Good air quality is increasingly becoming a key consideration by tenants when looking for a space to rent.

Indoor Air Quality can be affected by several factors, such as pollutants, thermal conditions, airborne viruses and the performance of HVAC (Heating, Ventilation, Air Conditioning) systems.

Indoor Environmental Quality (IEQ)

To assess IEQ, data needs to be gathered from across various points within a building and ideally analysed alongside BMS and HVAC operation, particularly when looking to improve IAQ which is differently affected by HVAC performance.

In this article we explain how Indoor Air Quality can be improved by using building analytics, which indicates where adjustments need to be made to assure good air quality for occupiers.

When present, your BMS (Building Management System) will be our primary data source but there are several reasons why we recommend a separate analysis tool:

Firstly, BMS systems are not very good at number-crunching because it's not really what they were developed for. The typical BMS controller (for example, the digital controller that is attached to each ceiling-mounted air conditioning unit) is excellent at reliable 24-7 control. Once programmed, such units faithfully cycle through a table of simple steps - predictably and (all being well) indefinitely. But in order to get such units to do other calculations (say to drive displays or test ideas), the whole strategy must be reprogrammed. This puts the main control task at risk and can over-complicate the controller's software, making it opaque to future controls engineers.

Secondly, the need for display and analysis is dynamic and unpredictable. We believe the most successful approach to eliminating energy wastage in buildings is for people to be free to explore new ideas or hunches, and be able to test them easily against real data. It is simply too time-consuming and expensive to have to reprogram individual controllers in order to see the results of new calculations.

By gathering the data into a central resource, Demand Logic allows for new ideas to be tried out instantly, and the results of investigations shared easily.

Thirdly, there is the matter of logging and storage. Demand Logic is currently (June 2022), streaming around 33 million data points each day, which equates to 12 billion data points annually. This is because most of the insights we provide require a full 'history' as well as live values. Modern databases make storing and operating on this time-based 'big data' very easy indeed. But we believe it is simply too much to ask for a control system to handle this task. It is true that some BMS systems provide a data-logging facility but again, you have to reprogram the specific controller (and use up its limited memory) in order to make use of this.

We advise leaving your BMS to do what it's good at (reliable 24-7 control of the equipment) and use a separate system for the data-crunching and display.

Many organisations are installing sub meters to track energy use of specific zones or equipment items. Sometimes, these are an ideal option, for example where the data is needed for billing purposes. However, sub meters can be very expensive - often as much as £1000 each when everything is taken into account.

If you are considering sub-metering because you want to find energy savings, we strongly recommend that you first make the most of the data in your building management system. There is so much detail in there showing when the equipment is running and what is likely to be causing high energy-usage. It just needs a little analysis.

Also, we find that "virtual meters" give a more granular picture as you can in principle track every single terminal unit (ceiling-mounted air-conditioning unit). Virtual metering is where you calculate the live energy using say, flow and return temperatures and flow-rates.

In our experience, the BMS equipment itself is hardly ever to blame for problems in buildings. Generally speaking, the leading brands provide highly configurable and reliable controllers, perfectly suited to controlling the plant. The problems are nearly always in how each controller is programmed and whether the mechanical systems and sensors etc have been properly installed or maintained.

In new buildings for example, when budgets inevitably run over, it is often the controls commissioning stage that gets pared down at short notice. This ends up with good BMS equipment going in but with sub-optimal programming (also called 'control strategies') applied to the controllers. Good ideas which may have been in the minds of the designers end up not being implemented in the controls. This can lead to a lack of faith in the BMS system as a whole -- with critics saying that even new systems don't work properly.

Also, in older buildings, the system quality can be eroded by a series of short-term fixes applied by different people. For example, a facilities team may quite rightly respond to complaints by putting certain items of plant into manual (also called 'in hand') as an emergency measure. But then the vital step of fixing the underlying control problem can be skipped and again people start to lose faith in the BMS itself.

But the good news is that in nearly every building we have looked at, there are low-cost improvements that can be made to the control strategies that lead to immediate and lasting energy savings and comfort improvements.

It is a common mistake to think that saving energy is a matter of measuring energy consumption. Yes, there are many projects that gather metering data, store it, and display it in beautiful ways. And of course, it can be a very important part of an energy-efficiency drive (especially in evaluating the success of projects).

But metering is only a tiny part of the story. If your building has a half-decent BMS, it will know when your boilers and chillers come on, when pumps run, if fans are left running, what each hot and cold water valve is doing - basically, you are probably in possession of a massive resource of clues about how your plant is behaving over time. And (with proper analysis and display) this is what leads to insights about how to reduce energy consumption. In fact, it is often true that preventing a particular wastage (for example, an extract fan running overnight) is actually easier than measuring its energy consumption.

In summary, if your analysis is based on metering data alone, no matter how clever it is (regression, degree-day correction, targets, etc) you will be missing part of the picture.