A 90.2 kWp rooftop solar plant at a multi-speciality hospital in Udaipur, Rajasthan was generating less power than it should have — but nobody knew exactly how much revenue was being lost, or why. Eighteen months after commissioning, the facilities team had inverter data but no way to benchmark it against weather-adjusted expectations. Then they onboarded to BijleeAI. Within one operating cycle, the platform had quantified the gap, identified the cause, and mapped a path to recovering ₹4.6 lakhs per year — without installing a single new sensor on the plant.
A Multi-Speciality Hospital, Udaipur, Rajasthan commissioned a 90.2 kWp rooftop solar plant to offset its daytime power consumption and reduce its grid electricity bill. Udaipur, located in the high-irradiance zone of Rajasthan, offers ideal conditions for rooftop solar — with a daily global horizontal irradiance (GHI) of approximately 5.5–5.8 kWh/m²/day. At the prevailing commercial tariff, the plant was projected to deliver an annual generation of roughly 1,59,000 kWh and offset approximately ₹15–18 lakhs in electricity costs per year, with a payback period of under two years.
Eighteen months after commissioning, the facilities team noticed that monthly generation figures from the inverter display were consistently lower than the projections in the original feasibility report — but without a dedicated monitoring platform, they had no way to quantify the shortfall, diagnose its cause, or determine whether the gap was within normal tolerance or a sign of a deeper problem.
Before onboarding to BijleeAI, the hospital's facilities team was operating the plant essentially blind. The core challenge was the absence of weather-adjusted benchmarking: the facilities team had inverter generation data, but no way to compare it against what the plant should have produced given actual irradiance conditions on any given month. This made it impossible to distinguish between unavoidable weather-driven variation and genuine performance losses attributable to soiling, shading, or equipment degradation.
When BijleeAI ingested the plant's historical generation data and overlaid it with satellite-derived irradiance data for the Udaipur location, the monitoring engine immediately identified a statistically significant and widening gap between expected and actual output. The performance ratio — a normalised metric that strips out weather variation — was running at approximately 78–80% in the months preceding the audit, against an industry benchmark of 82–86% for well-maintained plants in comparable irradiance zones. The gap mapped directly onto periods of low cleaning frequency.
BijleeAI's platform replaced the hospital's ad hoc cleaning schedule with a data-driven alert system. Instead of cleaning on a fixed calendar, the Cleaning Due Alert engine continuously monitors the performance ratio trend and triggers a cleaning recommendation only when the estimated revenue loss from continued soiling exceeds the cost of a cleaning intervention. This approach ensures that every cleaning event is financially justified and that no unnecessary cleaning spend is incurred.
Within the first operating cycle after onboarding to BijleeAI, the hospital's facilities team had a clear, quantified picture of their plant's performance gaps and a data-backed path to recovering them. The combination of optimised cleaning scheduling and the expansion opportunity analysis identified a total annual revenue recovery path of approximately ₹4.6 lakhs — without installing a single new sensor on the existing plant.
The hospital is currently evaluating the 30 kWp rooftop expansion, with BijleeAI's financial model submitted to its capital expenditure committee for approval. In parallel, BijleeAI's monitoring platform continues to run monthly anomaly detection scans on the existing 90.2 kWp system — tracking performance ratio trends, flagging deviations that exceed statistical thresholds, and generating automated monthly performance reports for the facilities team. As the plant ages, the degradation tracking module will provide early warning of any equipment-level performance decline, ensuring the hospital retains full visibility over its solar asset's contribution to its energy cost reduction programme.
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