Live operations. Measured savings. A clearer view of value.
June–August 2025 compared with the same winter months in 2024. This is a deliberately highlighted high-demand-season case study, not the lifetime average.
Less repetition, more meaning: outcomes that matter to operators and decision-makers.
A compact view of every measured month. Strong months stand out without letting one oversized chart dominate the page.
Measured actual cost versus what the same electricity-energy would have cost without the system.
Every historical month remains visible; the 2025 winter proof is highlighted separately, not substituted for the full record.
Battery support and avoided grid energy inside tariff peak windows.
How much expensive peak-period demand was supplied without drawing the same energy from the grid.
June–August 2025 is deliberately surfaced as the strongest high-demand season in the customer bill tracker.
From 1 July 2025 the winter evening peak became three hours instead of two, running directly through dinner trading.
Physical energy performance, separated from tariffs and cost escalation.
Customer-agreed forward model: future electricity-energy tariffs escalate at a fixed 10% per year, regardless of later published increases.
Capital recovery is important, but it is not the only proof point. Measured operating savings and peak protection remain the primary evidence.
How measured electricity-energy savings have accumulated since commissioning.
Every available measured month remains visible. Nothing is removed simply because the operating result was weak.
| Month | Saving % | Rand saving | PV | Load | Grid | Peak avoided |
|---|
What Northmead's energy profile looks like on the restaurant's busiest-energy days. Calendar events are context, not proof of sales causation.
If Nando's later supplies daily sales or transaction counts, this can become kWh per transaction, energy cost per R1,000 sales and true sales-energy correlation.
| Date | Calendar context | Restaurant use | Solar | Grid | Battery support | Peak avoided | Max demand |
|---|
Average restaurant energy by day of week across the available measured history.
Energy data can identify high-load trading patterns. Add daily POS sales or transaction counts and the portal can calculate energy per customer, energy per R1,000 sales, grid cost per transaction and whether busy days are becoming more energy-efficient.
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