For any business with thousands of distributed sites, energy is a fleet problem disguised as a billing problem. The bill says what was charged; the operational feed says what ran on grid, diesel, battery or solar. Until the two are joined, nobody can say which sites cost too much, or why.
Reconcile before you analyse
For a national telecom-tower company in India, we joined a month of electricity billing — over a million rows — with the operational record of run-hours by source. Some sites appeared in only one feed. Treating those mismatches as findings, not noise, became part of the product.
The Energy-360 score, per site
As built for a national telecom-tower company
| Component | Weight | What it rewards |
|---|---|---|
| Cost efficiency | 30% | Lower rupees per kWh |
| Reliability | 25% | Grid availability, fewer diesel hours |
| Sustainability | 30% | Solar share, lower CO₂ |
| Data quality | 15% | Billing and operations that agree |
Note: Weights are configurable.
Source: DaasLabs, “Delivered for energy, pipelines and heavy industry: energy case study” (2026)
From passport to decisions
With a passport per site, the fleet becomes a ranked list: solarisation candidates, the worst cost-per-kWh sites, and billing-versus-operations anomalies to chase. A stress lab recomputes cost, diesel volume and CO₂ for the whole fleet when diesel price, grid tariff, outage patterns, tenancy or solar rollout change.
Treat the gap between the bill and the run-hours as a finding, not a data-cleaning step. That gap is where the money is.
Where to start
Start with one month and one region, reconcile billing with operations, publish the mismatches, and agree the score weights with finance and sustainability before scaling to the fleet.