Skip to main content
Back to blog
Engineering9 min read0 of 9 min read

Getting the Most Out of Smart Meter Interval Data

Share:Share

Smart meters generate 15-minute interval data — that's 35,000+ readings per meter per year. This granularity unlocks analytics that monthly bills simply can't provide.

35,040Annual 15-minute interval readings per smart meter

Why this matters: A demand spike that lasts 15 minutes is invisible in monthly billing data but can trigger demand charges that persist for an entire billing cycle. Interval data is the only way to see — and manage — these events.

What Interval Data Reveals

Load Profiles: See exactly when your building consumes energy. Identify after-hours waste, peak demand contributors, and seasonal patterns.

Peak Demand Analysis: Understand what drives your demand charges — often 30-40% of a commercial electricity bill. Identify peaks that could be shaved with load shifting or demand response.

Real-Time Anomaly Detection: Spot consumption spikes within hours instead of waiting for the next monthly bill. A 15-minute spike that would be invisible in monthly data becomes clearly visible.

Demand charge strategy: Most utilities measure peak demand over a 15-minute window and bill for it all month. Shaving just one 15-minute peak per month can save thousands annually. Interval data is the only way to identify and eliminate these peaks.

Integrating Smart Meter Data with EdiMono

EdiMono supports multiple integration methods: 1. CSV upload — Green Button XML, standard CSV formats 2. Utility API — direct connections to Hydro-Quebec, Toronto Hydro, and others 3. BAS integration — pull data from your building automation system

Best Practices

  • Validate on import — check for gaps, outliers, and timezone issues
  • Align with billing data — reconcile interval totals against monthly bills
  • Start with load profiles — the quickest insight with the least effort
  • Layer in weather data — EdiMono auto-correlates with local weather stations

Key Takeaway

When importing interval data, always check for daylight saving time gaps and duplicates. A missing hour in March and a duplicate hour in November are the most common data quality issues — and they silently corrupt your baselines if left uncorrected.

Key Takeaway

Interval data is the highest-resolution view of your building's energy behavior. If you have smart meters and aren't analyzing the data, you're leaving insights (and savings) on the table.

Share:Share

Ready to put these insights into practice?

Request a Demo

More in Engineering

Getting the Most Out of Smart Meter Interval Data | EdiMono Blog