We were tired
of cleaning panels
the wrong way.
Integrated Solar Systems is a solar technology company. We're developing a patent-pending cleaning system for utility-scale solar arrays. Our thinking started in the field, where every kilowatt-hour earned during the day depends on the dust that fell the night before.
Solve the high cost
of manual cleaning.
A solar farm is a financial instrument. Dust is the spread that eats the yield. Manual cleaning is the maintenance bill that erases the spread.
Most utility-scale farms treat cleaning as scheduled maintenance, a fixed monthly cost regardless of weather, season, or local soiling rate. The crews arrive. They scrub the whole array. The water tank empties. Nobody knows whether half those panels needed the visit.
We spent time with cleaning crews in the field while the patent first took shape. The simplest insight in this whole company is that soiling is non-uniform. Cleaning, until now, has always been uniform.
Our system is designed to invert that. The model decides per zone, per panel, per fluid, per pass. The boom does only what the model asks. The result is meant to be cleaner glass, smaller water bills, fewer cycles, and a software trail that proves it.
- Machine-learned controlA predictive model decides what to clean, when, and with which regime.
- Per-zone activationNozzle output and boom carriage movement are controlled independently for each section of the array.
- Three spray regimesCooling pulse, light density, and heavy density, selected by the model per panel and per cycle.
- Learning modelThe system is designed to collect field data on every input and improve its decisions over time.