A basic sign of intelligence is the capability to learn, and that characteristic is clearly not present in today’s lighting solutions. This will change. The lighting systems of the future will use AI to varying extents and in different formats. Learning will happen through sensing and combining other data into logical conclusions.
The number of sensors in commercial spaces is predicted to grow significantly and as the data gathered moves into cloud-based platforms, learning and related activity will develop in several areas e.g. utilizing presence sensor data from a lighting system to control room temperature. As a result, lighting will have a more significant role in the construction and real-estate business than it does today.

Self-learning algorithms equals continuous auto-commissioning and machine learning have the potential to serve as an “expert on-site” and to help grow the adoption of controllable lighting. They may also be able to make lighting control operation “easier” and “less costly”.

AI can collect and analyses data on behavior patterns and predictions to help designers improve building environments and lighting, generating significant benefits and value to both the property’s user and owner. Having more data will turn the focus of design onto the people that occupy the building rather than the building itself.

This will benefit all users, leading to better buildings, which is ultimately more beneficial financially for building owners. AI can also help to make the initial configuration of the lighting system much easier and more effective which is a significant benefit for maintenance personnel and, more importantly, it can provide options for automatic or semi-automatic re-configurations, as well as tools to enable predictive maintenance. It is clear that in both the use and maintenance of lighting, AI will accelerate the development of solutions and services.

A continuous flow of data from lighting and other environmental sensors, users, events on the site and data from other external sources will create circumstances where “an expert” is monitoring and tuning conditions all the time. It creates greater transparency and brings light to the true value of information.
Using this data could, for example, help building owners to optimize energy consumption and, even more importantly, help to create a more human centric approach to lighting and the built environment. Increasing the wellbeing of a building’s users is the ultimate driver to use artificial intelligence, machine learning and self-learning to its fullest extent to tune lighting conditions.

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