This company provides a data-driven solution for proactive soiling mitigation in solar energy assets. They use machine learning and predictive modeling based on climate data to forecast soiling losses and recommend optimized cleaning schedules. The service helps asset owners maximize ROI by reducing unnecessary operations and maintenance spending.
Funding
$120K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
SAFounders
Product
Problem
Soiling, the accumulation of dirt and dust on solar panels, significantly degrades solar photovoltaic (PV) system performance, leading to reduced energy generation and lost revenue for utility and commercial assets. Traditional methods of monitoring soiling losses, such as IV curve tracing, are costly and time-consuming. This makes it difficult to optimize cleaning schedules and maximize profitability.
Solution
Solar Unsoiled offers an AI-powered monitoring solution that leverages machine learning and predictive modeling to mitigate soiling losses in solar PV systems. The platform continuously monitors site performance using operational data to isolate soiling losses and provide an up-to-date view of performance trends. It combines historical performance data with local climate and atmospheric data from NASA's global climate model to build site-specific models of future soiling losses. By factoring in power purchase agreements (PPAs) and cleaning costs, Solar Unsoiled generates ROI-optimized maintenance schedules, enabling proactive decisions and maximizing profit across entire fleets. The platform also offers a handheld microscope with a digital camera, providing technicians with a cheaper and faster alternative to IV-curve tracing for measuring soiling on-site.
Target Audience
The primary target audience includes energy asset owners seeking to maximize profit across their solar fleets, O&M service providers aiming to reduce the risk of missing performance guarantees, solar cleaning companies looking to simplify job planning logistics, and solar EPCs needing to provide soiling loss analysis during site commissioning.
Features
- Machine learning algorithms isolate soiling losses from operational data.
- Site-specific predictive models forecast future soiling based on historical performance and climate data.
- ROI-optimized cleaning schedules maximize profit by factoring in PPAs and cleaning costs.
- Handheld microscope with digital camera enables rapid on-site soiling measurement with ± 1.3% accuracy.
- Continuous monitoring of site performance provides up-to-date performance trends.
- Alternative to costly IV-curve tracing.