Automatic breakdown of gap between expected and measured energy, with 21 categories of availability and performance losses, of which 13 are recoverable losses leading to recommendations
Leveraging a physics-based digital twin, our advanced analytics drastically increases the sensitivity of underperformance detection. With lower threshold for identification of losses, issues are detected up to 12 months earlier.
Our recommendation engine identifies recoverable losses that went beyond a certain threshold, summarizes the losses per inverter and gives recommendations for further investigation. Losses can be sorted by lost energy, lost revenue or return on investment (ROI) for prioritization
Fully automated dashboards and reports to assess plant performance, compare to business plan, assess external, non-recoverable and recoverable losses.
At the core of the analysis is our physics-based performance model, which uses a Digital Twin of the plant.
Our performance model calculates predicted energy using historical weather data for a normal meteorological year and expected energy using measured weather data.
Based on the detailed monitoring data, our data-driven advanced analytics break down the gap between expected and measured energy in 21 different loss categories.
A subset of our losses are recoverable. The auto-generated recommendations point you to the corrective actions you can undertake to increase future yield.
Let your data work for you
Unlock peak asset performance with Solar Analytics: Harness the power of digital twin-based predictive analytics for asset optimisation.