TRACE
Tracing Energy Anomalies to Causes for Evidence-based auditing
Industrial organisations need to understand and reduce energy use across diverse processes, assets and operating environments. Conventional energy auditing can be difficult to scale across sectors and may not provide consistent, data-driven identification of inefficiencies and improvement opportunities.
The project will create a common AI-based auditing framework that can be applied across multiple industrial sectors. The approach will use energy and operational data to support structured analysis of energy performance, identify potential inefficiencies and generate actionable insights for audit processes. The innovation lies in combining artificial intelligence with a reusable cross-sector auditing methodology rather than relying only on manual, site-specific assessments.
The consortium will define the requirements for a multi-sector industrial energy auditing framework and identify the data, process and reporting needs common across industrial environments. It will develop and validate AI-based methods for analysing energy performance, detecting inefficiencies and supporting audit recommendations. The work will also include integration into a usable framework and assessment of its applicability across different industrial sectors.
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