AI-Driven Semantic IoT Gateway for Energy and Building Metering Systems

Wattega is launching this challenge to accelerate the deployment and scalability of energy monitoring systems in buildings and industrial facilities.

Today, the installation and configuration of energy and sub-metering devices remains highly manual, time-consuming and error-prone, especially when integrating heterogeneous field devices using industrial communication protocols such as Modbus, BACnet or similar. In addition, the lack of standardized semantic interpretation of captured signals significantly limits automation, interoperability and data quality at platform level.

The objective of this challenge is to develop an IoT device or gateway capable of:

• Interfacing with industrial metering and building automation systems using standard protocols (e.g. Modbus, BACnet, M-Bus, OPC-UA).

• Automatically detecting, classifying and interpreting signals through AI-based semantic inference.

• Mapping detected signals and devices to standardized semantic models and ontologies, with a strong preference for SAREF and/or BRICK.

• Minimizing manual configuration, commissioning time and human intervention during on-site installation.

• Reducing configuration errors while ensuring consistency, traceability and scalability across multiple deployments.

The solution should include an embedded or edge intelligence layer capable of learning from device metadata, register patterns, communication behavior and contextual information to automate both protocol configuration and semantic alignment with the cloud platform.

Additional technologies or approaches that contribute to automation, robustness and scalability are welcome, such as edge AI, digital twins, self-describing devices, zero-touch provisioning, or standardized device profiles.

Wattega is seeking solutions that can be validated through a real-world pilot, with a clear path towards industrialization and integration into its energy monitoring platform.