Avoid committing energy murder: Make your building smart from the outset

Big data is in – also within asset management – and you should exploit all the data you can get your hands on. All building owners – both large and small – are interested in how their buildings perform, for example in terms of energy efficiency.

Our vision is that if you connect the 3D building model with data from the intended building management systems (BMS) during the design phase, then you can create coherence between data sources from:

• The building’s automation systems – typically sensors, temperature, CO2, movement and energy meters, motorised valves and ventilation dampers
• The technical plant – e.g. heating, chiller and ventilation plant that provide HVAC to the rooms where the sensors are
• The shading regulating solar gain to these same rooms.
• The building’s access control system, which can contain information about the number of persons in the building
• Equipment such as coffee machines, work stations, etc.
• The buildings geometry

In this way, the data sources are contextualised with respect to their function. Furthermore, they can benefit from exchanging information about the building’s and the room’s usage, orientation, floor area and window area. The building owner can use this knowledge to ensure intelligent operation of the asset.

Avoid committing energy performance murder

Data in digital building models can provide insights and reduce CO2. By analysing data during operation of the building, you can get an overview of your usage and useful information can be fed back to the system, so the different systems regulate one another instead of the opposite. If you can prevent the different systems from fighting against each other, then you can prevent energy being slaughtered, for example where heating and cooling systems work simultaneously. This also gives you a much better basis for a pleasant indoor climate.

By combining data and building models, building owners get an analysis tool where they can save energy, avoid discomfort from the building services and ensure a better indoor environment for the employees. You get a different and more varied picture of the way the building performs, and you can document this with specific KPIs.

Collecting and analysing data is not limited to building operations; it can be used for a number of other purposes such as:

• Planning which rooms and spaces are to be cleaned based on registered use
• Optimising the use of rooms according to their user capacity
• Emptying refuse containers
• De-icing of roads and nearby areas
• Optimisation and control of parking
• Customer flow in shopping malls

Aarhus University wants data-driven maintenance, operation and decision-making

If you planning a construction project, you can make your house or building smart from the outset and identify unnecessary expenses before  the operational phase kicks in. Not only do we see a potential for intelligent building management for new assets, there are also plenty of opportunities with existing buildings. At the moment we are helping Aarhus University to gain a better overview of their data.

Aarhus University has started a project where they use data from their existing BMS and energy registration systems. We help them put these data into context – for example, where a digital temperature sensor is placed in relation to the incidence of daylight in the east or west wing. By putting various types of data in a database, the university is capable of cross-analysing the information provided.

Aarhus University’s motivation is to be able to enable much more data-driven maintenance and operation in general, whilst also making data-driven decisions, thereby saving resources. With a better overview of their data, they can analyse and check if they comply with statutory requirements, for example with regards to indoor environment parameters, together with seeing if their buildings stand out in terms of energy and water consumption.

They gather information about occupancy (number of people) in the classrooms and by doing this they can optimise the use of the rooms with regards to the actual number of students in attendance.

Raw data has a limited value (A). When we have knowledge about different types of data and the context they take part of, we have useful information (B). When we can set data in connection with other data and associated systems, we have knowledge that can be used across different platforms (C). When we can analyse the fact that there is data that stands out in regards to the other data, we have insight (D). When we can connect all of this and gain data driven help to see through the complex connections where data and associated systems affect each other, we get smarter (E).

An intelligent guide for the service personnel

Big data provide many new possibilities and opportunities. We use building models in our projecting that contains technical installations and sensors. Models can be used to provide a tool containing the needed information from the beginning. You no longer have to manually provide information about the connection between different room sensors and which ventilation- and heat installations are operating the rooms.

The systems can work as an overall guide for the service personnel. Instead of them having to detect the irregularities, the system can work as a form of intelligence that lets the service personnel know what is wrong and where they need to do something. However, the service personnel can also, based on the analysed data, send signals back to the BMS if there are some things they need to change, e.g. cooling at night.

There are many opportunities for improvements, and not only in regards to saving energy and ensuring a better indoor climate.

Based on a PhD project, NIRAS has knowledge about how data should be structured in order to use it in the most effective way. We basically pour a bunch of information into a funnel and get an extract out that is searchable and easy to handle in a network of threads where everything is connected.

The development has only started. There is data for everything, and we intend to use this in our buildings.

How to make your building smart from scratch in the projection phase:

1. Create insights in your building from the beginning. There are many potential problems with getting things to work. If you use intelligent management when your building starts operating, you can detect a lot of the problems in the commissioning phase. The cost of implementing intelligent management from scratch is small comparted to what it would cost to have it implemented later on in the process.
2. Think in context. What should the building be used for? Is there any data that is especially important in order to fulfill the wishes for the building or business – like usage percentage of rooms? Find out if the data is already accessible in the systems that are installed in the building. If not, then you can ensure that there are sensors to measure the necessary data in the projection phase.
3. Have a talk about what you want to achieve with the system. Is it e.g. more nuanced KPIs? The system can be scaled from larger building owners’ overview of many buildings too smaller building owners’ usage dependent of wishes and goals.

What can NIRAS offer?

As projecting engineers, NIRAS can - in connection with a building project - work out an expanded building model that contains more information than normal. You can use the model to create the necessary big-data contexts between data measurement points, the technical installations, and rooms.