Placed on – 29 June 2021

The four advantages of a data-driven building

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Data-driven-building

The digital revolution makes it increasingly easy for construction companies to collect, analyze and gain new insights. New insights often arise in many more areas than initially thought. What can you think about when you want to get started with data?

Where construction companies used to say goodbye after a construction project, they increasingly remain involved in management and optimization. This involvement makes data a significant component to create and maintain sustainable, pleasant, and cost-efficient buildings. Memoori predicts that the global use of IoT in buildings will increase by about 10% per year in 2019-2025. Occupancy tracking sensors and devices are expected to experience the highest growth rates at 14 percent per year. What benefits does all this offer for construction companies?

1. Achieving sustainability goals
Housing associations, business users of buildings, and investors are beginning to see that data can help them realize the paper sustainability goals in practice and thus save a lot of money. The willingness to think about this is great. It is increasingly leading to IoT becoming part of the specifications. The business case is usually made based on the NZEB standards. And the estimation of the potential returns is an important factor in this. With IoT, you can measure the actual energy consumption, and you can check whether the objectives can be achieved and whether the subsidy can be awarded. The US Department of Energy (DOE) and the Building Technology Offices (BTOs) have calculated that annual energy savings in the commercial sector can reach 29 percent by implementing measurement and monitoring systems, state-of-the-art sensors, controls on the operation of climate control systems, and predictive maintenance on these systems.

2. Predictive Maintenance
Most equipment used to heat or cool buildings already comes with sensors that measure quantities, for example, the water pressure or pump speed of a heating boiler. They give a signal if it goes above or below a certain threshold value. You can also go a step further and combine different metrics. A problem often presents itself with small deviations. For example, the supply or return temperature of the water in a heating boiler starts to deviate, or the power consumption of the boiler increases. If you measure various factors together, the individual indicators may remain below the threshold value, but the pattern tells you that maintenance is still required. Maybe not today, but in the next three months. These insights mean that you can plan maintenance much better, make better use of scarce resources and that you can prevent operational failures.

3. Actively report misuse
The combination of different indicators also provides a better understanding of how certain equipment is being used. How is a boiler adjusted, and does it match the type of heating used in a building? It happens regularly that high-tech equipment is misused so that the promised energy savings are not achieved.

4. Better insight into the use of a property
You can go one step further if you have all the data – many hundreds of measurements per day from tens of thousands of sensors – searched by smart machine learning algorithms that look for patterns on their own. That way, you discover correlations you didn’t know existed, for example, about the use of the building. When you use infrared sensors or image recognition to measure whether someone is present in a room, you gain insight into the actual occupancy of a building. Information can often be derived from this occupation that can help to optimize the building. For example, it is known that the best meeting rooms are the most used. This information can therefore lead to initiating improvements, optimizing cleaning schedules, and reducing energy consumption. There are examples of organizations that have been able to postpone new construction because they were able to increase the occupancy rate at the existing location without loss of quality and user experience.

Once you start with machine learning or comparable techniques to extract insights from data, this often leads to a broadening. One insight provokes another because people start asking questions: if we can measure this, can we also process that in the dashboard? In short, you gain benefits in completely different areas than the ones you initially used IoT for. Which areas are those? That depends entirely on the building, the type of use, and your creativity to do more with data. But experience in other sectors shows that savings opportunities will arise in areas that were not anticipated beforehand.

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