The most cost-effective approach is usually:

1) Start with metering only where it changes decisions

Don’t instrument every device on day one. Use a tiered metering strategy:

• Site level: utility/main feeders for total energy by location
• Major loads: HVAC, lighting panels, production lines, EV charging, etc.
• Only critical devices: equipment that is expensive, variable, or targeted for optimization

This gives you most of the value for the least hardware and installation cost.

2) Use the existing electrical infrastructure first

Before buying new sensors, check whether you can pull data from:

• Existing BMS/SCADA
• Smart meters already installed by the utility or panel
• Submeters already on key circuits
• PLCs or controllers with energy data
• IoT gateways connected to existing meters

Leveraging existing data is usually far cheaper than retrofitting every device.

3) Prefer wireless or non-invasive metering where possible

For device-level tracking, cost is often driven by labor and downtime, not the meter itself. To reduce cost:

• Use clip-on CT sensors or non-invasive meters for many loads
• Use wireless submeters if running new wiring is expensive
• Choose devices with Modbus/BACnet/BLE/Zigbee/LoRaWAN depending on your environment

These reduce installation time and disruption.

4) Standardize on one platform with flexible data ingestion

Pick an energy management platform that can:

• Ingest data from multiple protocols
• Normalize site/device hierarchies
• Handle both utility bills and real-time meter data
• Support dashboards, alerts, and API access

A platform that integrates well saves a lot more over time than a cheaper system that requires custom work.

5) Use a phased rollout

A low-cost rollout typically looks like this:

1. Phase 1: Site-level monitoring at all locations
2. Phase 2: Submeter top 20% of loads causing 80% of usage
3. Phase 3: Add device-level monitoring only where ROI is clear
4. Phase 4: Automate alerts, anomaly detection, and reporting

This prevents overspending on unnecessary granularity.

6) Choose the right data resolution

You don’t always need high-frequency data.

• 15-min intervals are enough for many sites and reporting use cases
• Higher-resolution sampling is useful only for troubleshooting or process optimization

Lower data volume usually means lower hardware, storage, and analytics costs.

7) Focus on ROI-driven use cases

Track only the sites/devices where energy visibility leads to action, such as:

• Identifying wasted runtime
• Peak demand reduction
• Faulty equipment detection
• Tenant or department cost allocation
• Production energy intensity tracking

If the tracking won’t drive a decision, it probably isn’t worth the cost.

Best-practice cost-effective setup

If you want the shortest answer:

Use utility/main meters for every site, submeter only the highest-energy or most variable circuits, reuse existing building/system data wherever possible, and connect everything to a platform that supports open protocols and phased expansion.

If you want, I can also give you:

• a recommended architecture
• a vendor selection checklist
• or a budget estimate by site/device scale