From Monitoring to Real-Time Operational Insight

Legacy energy management platforms have historically been used to collect and display consumption data, which then required manual interpretation by site managers or consultants. Decisions about efficiency measures were typically made reactively and periodically, often after operational impact or cost increases had already occurred. By contrast, AI-enabled energy management systems apply real-time data analysis and pattern recognition to identify irregular usage or developing issues as they happen. These systems can rank energy-saving opportunities by value (e.g., kWh, £, or CO₂e) and support active interventions before performance declines. This continuous mode of operation allows for faster corrective action and the potential for more consistent energy performance across processes and timeframes.

Embedded AI Capabilities

ClearVUE’s approach includes embedded AI tools within its energy platform, such as ClearVUE.Iris — a module designed to interpret usage data, surface inefficiencies, and suggest remediation steps. These suggestions are assigned an estimated impact and can be tracked through completion. For example, the system can detect excessive out-of-hours load, identify power factor irregularities, or highlight shifts in baseload that may signal process drift. Users are able to assign fixes, track progress, and see post-action baselines to verify impact. In addition to immediate operational benefits, AI-assisted energy systems can simplify reporting requirements under frameworks such as SECR or Streamlined ESOS by producing structured data and performance logs suitable for audit and board-level use.

Adoption in Manufacturing Contexts

Manufacturing facilities, often characterised by complex operations and energy-intensive machinery, stand to benefit from more adaptive and responsive systems. AI-supported platforms offer an alternative to static dashboards and allow teams to maintain steady operational performance, mitigate unexpected costs, and extend visibility across circuits, sites, and assets. One feature of ClearVUE’s platform is its modular structure, which allows organisations to scale their use of AI tools as their needs evolve — integrating predictive forecasting, opportunity ranking, and automated reporting when required. Some manufacturing organisations using AI-assisted systems have reported energy use reductions of up to 60%, depending on the facility type, usage profile, and the measures implemented. Outcomes vary based on existing operational efficiency and the level of intervention made.

Outlook for Intelligent Energy Systems

While adoption is still scaling, AI energy management systems are increasingly being integrated into the wider digital infrastructure of manufacturing operations. Their ability to continuously analyse and respond to data, rather than simply collect it, is shifting energy management from a retrospective function to a real-time operational control mechanism. At MENE 2025, ClearVUE will demonstrate how AI can be applied to energy use data in practice — through live dashboards, example scenarios, and platform walk-throughs. Manufacturers attending the event can learn more by visiting Stand 93 on 27 November at the Vertu Motors Arena in Newcastle.