Solar: knowing which string is losing you money, today

Performance ratio as a monthly plant-level number is a contractual metric rather than an operational one. Computed at string level, normalised for plane-of-array irradiance and module temperature, and compared across a plant, it becomes a map of where losses are concentrated.

We build that computation from inverter and string monitoring data joined with the weather station, and we present it as a ranked list rather than a chart: the fifty worst-performing strings this week, with an estimate of the generation being lost and a probable cause category. That turns an analytical exercise into a work order.

Because the normalisation is done properly, seasonal and weather variation is removed, which means a genuine degradation trend is visible early rather than being lost in the noise of a monsoon month.

Three loss mechanisms look similar in aggregate output and require completely different responses. Soiling is recoverable by cleaning and follows a pattern driven by dust, pollen and rainfall. Shading is structural and time-of-day dependent. Degradation is permanent and should follow a slow, predictable curve.

We separate them using the signature each leaves in the data. Soiling produces a gradual decline across a whole area that recovers sharply after rain or cleaning. Shading produces a repeatable time-of-day pattern that correlates with sun position. Degradation shows as a persistent offset that does not recover.

The practical output is a cleaning decision supported by numbers: the soiling loss on a given block has reached a point where cleaning cost is justified. Most plants clean on a calendar; plants that clean on measured soiling loss recover meaningfully more generation for the same expenditure.

Every solar asset produces a daily generation report, and at most sites somebody assembles it manually from the SCADA export, the meter reading and the weather station. It takes an hour or two, it is inconsistent between sites, and it is late.

We automate it completely: data read from the plant systems, computed against your definitions, and published in your exact format to a shared location or emailed before the reporting deadline. For portfolios, all sites produce identically structured reports, which finally makes fleet consolidation something other than a manual reworking exercise.

PPA compliance packs follow the same pattern. Availability computed against the contractual definition — which is rarely the same as raw uptime — deemed generation for grid-curtailed periods, and the supporting evidence assembled automatically. For assets where availability guarantees carry penalties, having this computed consistently and defensibly is worth considerably more than the labour saved.

Day-ahead and intra-day generation forecasting matters for scheduling and, in markets with deviation settlement, for avoiding penalties. It also matters increasingly for hybrid assets managing storage dispatch.

We build forecasts from numerical weather prediction inputs combined with the plant's own historical response — because two plants under identical forecast irradiance generate differently based on their configuration, soiling state and inverter behaviour. Accuracy is tracked continuously and reported, because a forecast whose error nobody measures is not a forecast.

Typical day-ahead accuracy after a few months of learning is within four per cent on a monthly aggregate basis, which is sufficient for scheduling purposes and materially better than the generic forecasts many operators rely on.

Distributed portfolios — hundreds of rooftop installations across many customers — have a different problem shape. Individual assets are small, instrumentation is minimal, and site visits are expensive relative to the value at stake.

Here the value is in exception detection at scale: identifying which of four hundred rooftops is underperforming relative to its own history and its neighbours, so that a site visit is dispatched only where it will pay. We build the comparative analysis and the alerting, plus a customer-facing portal showing generation and savings, which materially reduces support calls.

For companies selling under OPEX or RESCO models, we also handle the billing complexity: generation-based invoicing, escalation clauses, and reconciliation against net-metering credits.

Solar clients typically also need the commercial layer — O&M work order management, spares, warranty claims against inverter and module suppliers, and the ERP tie-in for a company operating multiple assets. We build these on the same platform so operational and commercial data sit together.

For developers, we also build the pre-construction side: resource assessment data management, yield estimate versioning, and the document control that a lender's technical adviser will eventually want to inspect.