(built into a RealTime monitoring platform)
Improve Generation – 2 – 4%
Reduce O&M Costs – 15%
Return on Investment (RoI) – 10X
For Solar & Storage Assets
Statistical models cannot predict how each plant will behave under different operating conditions.
of your Solar or Storage plants are the answer. Then we can model precise yield capabilities in exact field conditions. First creating highly accurate models to predict the behavior of each inverter, each SCB, each panel string – we can add them bottom up.
Using data, we analyze (while monitoring) the past and present and simulate the future in RealTime. Time Travel allows us to see signals early, so they’re addressed proactively with Automated, Revenue Prioritized Tickets.
of Digital Twins combined with Prediction and Anomaly Detection AI. These go beyond simplistic models, to multiple, hierarchical, purpose-specific Digital Twins, which are much better at identifying errors and anomalies much before they happen.
Automated asset operations with built-in Predictive Analytics in a RealTime platform for Data Centralizing | Monitoring | Reporting | Visualization
explain what we do, in their own words.
explain what we do, in their own words...
We worked with Quadrical to build a data platform, consolidating our plant data onto a common schema. Quadrical built out the schema with Al capabilities to help 0&M teams to identify and rectify anomalies, prioritised by revenue impact. There are many players who claim capabilities and tools in Artificial Intelligence, but none have the true depth of technical abilities that Quadrical brings to the table.
As we onboarded 25 plants with Quadrical in 45 days, we’ve been impressed with their ability to fully own data quality and connectivity. With advanced Analytics-as-a-Service, we’re already seeing the benefits with insights available for our operations team to identify the plant performance issues and possible breakdowns proactively. I am now confident that this platform has the potential to transform the way we do our operations.