Industry-leading software to monitor, analyze, control and maintain industry scale solar PV farms.
"Professional and user-friendly – Bright meets all of our monitoring needs"
- SARTELCO BIOMASSE S.R.L., ITALIAN SOLAR PLANT OWNERRead more
Bright is the solar plant management component of Energy Cloud. A cutting-edge specialized software used by industry-scale owners, operators and investors to monitor, manage, analyze and plan every aspect of their solar PV portfolio.
Monitor real-time energy production on all levels.
Analyze and compare solar data across the portfolio.
Plan activities such as tasks, downtime and site access.
Configure users, assets and settings to suit your workflow.
Create reports, CSV exports and share data through the APIe.
Use Bright on desktop, in control rooms and on mobile.
Bright is a modern, user-friendly software solution that allows professionals to increase ROI, improve solar PV performance, and enable proactive solar PV plant management.
Monitor remotely your entire fleet of solar panel devices including individual inverters, oversee production data, warnings, alarms and many other key metrics in real-time and get notified of critical events.
Overview all solar plant activities, schedule O&M tasks and downtimes. Access all information, log comments, assign tasks to colleagues and inform them via automatic email update.
Perform in-depth data analysis and find the root to low performing solar panels. Follow up on trends, measure, visualize and compare lost production per portfolio, asset and inverter.
Take advantage of a versatile system and make use of the flexible widgets and dashboards to get a perfect summary of key figures, build custom charts, tables and highly configurable reports.
Check out our videos and get a glimpse into how we do things at Greenbyte.
Predict is a potential game-changer for the industry. At Greenbyte, we aim to detect component failures in wind turbines long before they occur using existing SCADA data. In this webinar, Head of research, Dr. Pramod Bangalore presents our approach to solving this difficult problem using large data sets and Artificial Neural Networks. Sign up!
Wed Aug 29
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