🌞Heliocity at the cutting edge of AI in solar energy diagnostics
How can I perform preventive maintenance to avoid costly inverter failures, or replace faulty modules before they provoke losses across a significant part of my solar array?
If these are questions that preoccupy your operations, Heliocity has the answer!
Advanced Remote Diagnostics for Solar Performance
Heliocity’s remote diagnostics services are built upon a solid foundation of physical numerical models and a detailed knowledge of solar technologies and the environment conditions in which they operate. These afford an unparalleled level of precision for the detection of performance anomalies and we capitalize on the information available in monitoring data through an appropriate use of data science methods. Machine learning has thus been an integral part of Heliocity’s analytics from day one.
Pioneering AI-Powered Diagnostics
Today we are proud to be pushing the frontier of artificial intelligence integration into physics-based diagnostics, with the development of new tools and services trained on the high-quality labelled datasets generated as part of Heliocity audits. Indeed, a unique feature of our efforts in this field is the construction of a substantial database of performance data, faults and loss factors on real installations.
In 2025 these cover already several hundred large, medium and small photovoltaic installations including building integrated and ground mounted systems, each one analysed over months or years down to a temporal resolution of minutes and a spatial resolution extending from inverter inputs to in some cases individual panels.
Leadership in Research & Predictive Maintenance
As a work package leader within the Solaris Horizon Europe research project (https://solaris-heu.eu/), Heliocity is leading the development of novel predictive maintenance service to manage fleets of solar installations, whilst exploring the opportunities to enhance field operations guided by Heliocity’s automated fault detecting and analytics and integrating on-site inspections into regular remote diagnostics.
To this end we are building upon machine learning and deep learning methods for the identification of fault precursors, the construction of digital twins, and the automation of action recommendations. Also, thanks to the Solaris project we are investigating the value added to solar monitoring services through the introduction of novel sensors and diagnostic measurements.
Let’s Talk
If you would like to know more about our innovation in AI, or would like to discuss integrating Heliocity’s automated diagnostics, predictive maintenance and enhanced field operations to improve the performance of your solar fleet, we’d be delighted to hear from you!