
The lighting industry commonly employs novel techniques in lighting design and control. Significant transformative changes within the sector include the evolution of the light bulb and the introduction of inter-networked lighting components that implement protocols such as DALI.
In the context of the Lighting Industry, the scope for applying AI is intriguingly broad, impacting the various stages involved in the lighting life-cycle such as design, installation, commissioning, and configuration.
For example, a self-learning network of lighting components can communicate and set-up itself without requiring human intervention similar to auto-commissioning systems used in the IT industry. This network will decrease the time needed to commission new lighting installations.
By observing and measuring the indoor environment, an AI-based lighting system can optimise and tune light parameters accordingly to impact user experience and well-being.
The utility of such a system is not limited to end-users or tenants but extends to other stakeholders such as building owners and facility managers as well.
A data-driven network of lighting components continuously generates data which is collected and stored at a centralised server. AI algorithms can run at the source component, such as a sensor, for decentralised, real-time decisions, or at a server for making centralised decisions.
Furthermore, the collected data applies to other Building Management Systems (BMS) such as Heating, Ventilation and Air Conditioning (HVAC), or access management.

