Metering data
Article 4 of 10 · SMEs, dashboards and controlDo I need expensive sensors to save energy?
Direct answer
Expensive sensors are not always needed to save energy. Often you can start with smart meter data, quarter-hour values and site context. Additional sensors or submetering only become sensible when you want to explain a specific process, machine or installation separately.
- Use existing data first
- Deploy hardware purposefully
- Avoid unnecessary sensor investments
- Submetering where it adds value
Installing sensors first versus analysing data first
Many organisations think energy saving starts with extra hardware. Sometimes that is true, but often the first value already lies in existing data. By finding patterns and anomalies first, you can deploy sensors more purposefully.
- Start with existing metering data before buying hardware.
- Use analysis to determine where detailed measurement is really needed.
- Submetering is valuable, but not always the first step.
Start
Traditional approach
Buy extra hardware right away.
Modern approach
Analyse existing metering data first.
Costs
Traditional approach
More investment upfront.
Modern approach
More precisely determine where detailed measurement is needed.
Insight
Traditional approach
Lots of detail, but possibly little priority.
Modern approach
First determine which questions matter.
When are sensors not needed?
If smart meter data reveals enough patterns, you can already start with night consumption, peaks, site comparison and cost analysis.
- Quarter-hour values are available.
- The question concerns the whole site or meter.
- Initial anomalies have not yet been investigated.
- The budget must stay limited.
When are sensors worthwhile?
Sensors become worthwhile when you want to know, within a building or process, which machine, installation or zone is responsible for consumption.
- Process or machine level is needed.
- Multiple installations sit behind one meter.
- Measures require proof at a detailed level.
- Real-time control is being considered.
Curious what this looks like with your own data?
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