Practical Functions of AI in Energy Management

Modern AI-enabled systems integrate multiple functions that previously required manual oversight or were not feasible with legacy energy monitoring setups. These include:

- Data integration: Consolidating live readings from meters, sub-meters, sensors, and schedules into one environment • Anomaly detection: Identifying unusual patterns in energy use that may indicate operational drift, maintenance issues, or inefficient controls

- Forecasting and adjustment: Modelling expected demand and adjusting controls pre-emptively to avoid excessive costs or equipment strain

- Communication and reporting: Translating raw data into prioritised actions, costed insights, and structured outputs for technical and non-technical teams

These capabilities allow operational and finance teams to identify and act on inefficiencies before they escalate, rather than relying on retrospective analysis of bills or monthly reports.

Sector Use Cases and Measurable Outcomes

AI-driven energy systems are increasingly being applied across manufacturing, logistics, hospitality, education, and the public sector. Reported benefits from early adopters include:

- Up to 40% reduction in equipment downtime via AI-enabled predictive maintenance

- Up to 15% peak load reduction through demand-response and dynamic controls

- 10–15% operational cost savings from AI-guided adjustments and reduced waste

Examples vary by sector:

- In manufacturing, AI systems can detect early signs of load inefficiency in production lines and trigger alerts before failure or excess cost occurs.

- In hospitality, automated adjustments to HVAC and lighting based on occupancy data can reduce usage without affecting service quality.

- In education, AI-backed reporting tools can simplify sustainability tracking and support planning based on verified data.

Role of ClearVUE in the Transition

ClearVUE is one of the companies developing these technologies for high-usage sectors. Its ClearVUE.Zero platform includes an AI extension suite capable of analysing consumption data alongside external factors such as tariffs, network charges, and schedule variations.

At its centre is ClearVUE.Iris — an AI module that ranks improvement opportunities by energy, cost and carbon impact, and produces costed insights for operations, finance and sustainability teams. For example, it can detect rising baseload or persistent out-of-hours use and attribute that consumption to specific circuits or timeframes.

The system also supports automated reporting aligned with internal governance or regulatory frameworks such as SECR, helping teams reduce manual processes and improve accuracy.

Looking Ahead

Adoption of AI-supported energy management is expected to increase over the next three years, particularly in energy-intensive and compliance-exposed sectors. As these tools evolve, they may increasingly interact with other operational systems (e.g. maintenance, building management, procurement) to create wider process efficiency and better resource coordination.

ClearVUE will demonstrate its platform and AI capabilities at MENE 2025 in Newcastle. Attendees can view live examples of site-level analysis, forecasting models and governance-ready outputs on Stand 93.