Energy Consumption Record

The plant gets a monthly utility bill — total electricity, gas, and water for the entire facility. Nobody knows which production line consumes what. When the CFO asks 'why did our energy bill spike 20% last month?' the plant manager guesses: 'We ran the heat treat furnace more than usual, maybe.'

AI cannot optimize energy usage because consumption data exists only as a single aggregate number. There is nothing to optimize against.

Sub-meters exist on a few major pieces of equipment — the compressor room, the heat treat furnace, and the paint booth. A facilities engineer reads the meters monthly and enters values into a spreadsheet. The rest of the plant is calculated as 'total bill minus metered equipment.' Energy reports are rough allocations, not precise measurements.

AI can identify the biggest energy consumers from sub-meter data, but coverage is too sparse for production-level optimization. Most consumption is an undifferentiated 'everything else' bucket.

Sub-meters cover all major production lines and large equipment. An energy management system collects readings at 15-minute intervals and stores them with equipment ID, meter type (electricity, gas, water, compressed air), and timestamp. An energy manager can report consumption by line and by month. But the data isn't linked to production volumes — high consumption could mean high production or equipment waste.

AI can identify consumption trends by equipment and time period. Basic anomaly detection — 'Compressor 3 is using 30% more power than last month' — is possible. Cannot determine energy per unit produced.

Energy consumption records are stored in a structured system linked to production orders, equipment, and operating schedules. Each record captures meter ID, energy type, consumption value, time interval, linked production order, and output quantity. An engineer can query 'what is the kWh per unit for Product X on Line 2 versus Line 3?' and get a reliable answer.

AI can optimize energy intensity by product and line, identify equipment operating inefficiently, and recommend scheduling changes to shift energy-intensive operations to off-peak tariff windows.

Energy consumption records are schema-driven entities with explicit relationships to equipment, production orders, operating conditions, tariff schedules, and environmental factors. Each record has a machine-readable context: what was produced, at what speed, in what conditions, at what energy cost. An AI agent can ask 'what would be the energy cost impact of moving the heat treat batch from Tuesday afternoon to Sunday night?' and get a precise financial answer.

AI can perform autonomous energy optimization — scheduling production for minimum energy cost, detecting equipment degradation from consumption anomalies, and managing demand charges. Full closed-loop energy management is possible for routine operations.

Energy consumption records are real-time streams from smart meters at every significant load point. Consumption data flows continuously, linked to live production state, equipment operating parameters, and grid pricing signals. The system knows the energy cost of every production minute on every line, updated in real-time. Energy management is a live optimization, not a monthly report.

Fully autonomous energy management. AI optimizes consumption in real-time, responds to grid pricing signals, manages demand peaks, and minimizes energy cost per unit produced without human intervention.

Ceiling of the CMC framework for this dimension.