Edge AI is rapidly advancing in the energy sector, bringing innovative solutions that enhance energy management systems’ efficiency, reliability, and sustainability. Integrating AI at the edge, particularly in devices like energy gateways, smart meters, and LED lighting systems, allows for real-time processing and decision-making, reducing latency and increasing security and privacy.

The development of AI-based energy systems is a rapidly evolving area driven by the need for more efficient, sustainable, and reliable energy solutions. These systems leverage edge AI to optimise energy usage, integrate different energy loads, sources, and appliances effectively, and enhance the overall energy management of these devices.

Edge AI enables energy management systems to become self-optimised systems using real-time data to adjust settings for optimal energy use without human intervention. At the same time, AI algorithms can predict when components within the energy system might fail or need maintenance, thus avoiding downtime and reducing maintenance costs.

The development of AI-based systems advances in AI-driven energy gateways that automatically adjust the power usage of connected devices based on the loads, sources, and utility signals to reduce load during peak hours, enhancing energy system stability. Edge AI is used in home energy gateways to manage and optimise energy flow from local renewable sources, balancing it with energy supply and storage capabilities in the residential, commercial, or industrial context.

Using historical data and real-time input from IoT devices, AI models can predict short-term energy demand, helping utilities manage load and generation effectively. The edge AI systems can incorporate real-time weather data to forecast energy needs more accurately, especially for systems heavily reliant on weather-dependent resources like solar and wind power.


Edge AI enables LED lighting systems to adjust brightness and colour temperature based on real-time environmental inputs like natural light availability or room occupancy, enhancing comfort while reducing energy use.

Health- and environment-focused lighting concepts are implemented using AI-driven LED systems that can vary lighting conditions to support human circadian rhythms, potentially improving health and productivity in workplaces and homes.

Intelligent adaptive lighting systems use LED lighting management together with wireless and wired networked lighting control. Edge AI facilitates the management of extensive networks of LED fixtures, optimising energy use and maintenance across residential and office buildings. These systems can predict and adapt lighting based on usage patterns and occupancy, ensuring optimal lighting conditions and energy efficiency.

Wireless lighting management systems allow for the remote and automated control of lighting fixtures using various wireless protocols. This approach provides enhanced flexibility, scalability, and energy efficiency.

In residential and commercial buildings, wireless lighting management systems can be integrated with other intelligent systems for enhanced automation. Lights can adjust automatically based on occupancy, ambient light levels, or preset schedules.

Wireless communication technologies in lighting management offer significant advantages in energy savings, operational efficiency, and user-centric control. As these technologies evolve, they will become even more integrated into the infrastructure of smart cities, buildings, and homes, further enhancing the capabilities and benefits of wireless lighting management systems.

Edge AI-driven and wireless innovations in energy systems represent a significant step forward in managing and optimising energy usage. They help reduce operational costs and carbon footprints and play a critical role in enhancing the adaptability and resilience of energy infrastructures. As edge AI technology continues to evolve, its integration into energy management will likely become more sophisticated and widespread, heralding a new era of intelligent energy solutions.

Blog signed by: SINTEF team

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