Accurate design and yield assessment demonstration
The report showcases the TRUST-PV initiative's advanced digital tools, including PVcase Yield, which optimize utility-scale PV plant design and energy yield assessment by validating against measured data and industry software, considering topography and system configurations, and demonstrating financial feasibility, with testimonials highlighting PVcase's support, efficiency, and global standardization trusted by leading solar companies worldwide.
Download the report, which delves into optimizing PV plant design and energy yield (EY) assessment through cutting-edge digital tools created under the TRUST-PV initiative, including PVcase Yield.
The EY tool is validated against measured data and compared with industry-standard software, assessing the influence of topography and system configurations. Plus, a financial feasibility analysis illustrates the tool’s utility in planning utility-scale PV projects.
Dive in to learn more.
Testimonials
“The support level is excellent. On top of that, when we have feedback or suggestions, PVcase is actually listening.”
— Peter Knittl, CEO
“PVcase supported us and helped us to speed up the design process.”
— Hannes Elsen, Engineering Team Lead
“PVcase Ground Mount has helped our company in terms of time efficiency and global standard in all the engineering teams.”
— Sergio de la Torre, Strategic Transactions Engineering Lead
Trusted by leading utility-scale solar companies worldwide
See how engineering and development teams use PVcase to design faster, reduce risk, and scale projects with confidence.
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Yield | PVcase
PVcase Yield is an all-in-one solar project platform that delivers precise, physics-driven yield projections and site optimization through seamless data integration, digital twin simulations, cloud computing, and 3D visualizations, significantly reducing manual work and project development time while providing detailed, customizable reports and strong customer support.
New Leaf Energy verkürzt Entwicklungszeit für Solarprojekte um 50 % mit PVcase
New Leaf Energy, ein US-amerikanisches Solarentwicklungsunternehmen, verkürzte durch die Integration der PVcase-Plattform seine Projektentwicklungszeit um 50 %, indem es den langwierigen Prozess der Standortwahl, Entwurfsoptimierung und Ertragsanalyse von mehreren Wochen auf wenige Tage reduzierte und so effizientere Teamarbeit, schnellere Einarbeitung und datengetriebene Entscheidungen ermöglichte.
PVcase Yield
PVcase Yield ist eine End-to-End-Plattform, die durch physikbasierte Simulationen, Digital-Twin-Technologie, Cloud-Computing und maschinelles Lernen präzise, schnelle und kalibrierungsfreie Ertragsprognosen für Solarprojekte liefert, dabei nahtlos Konstruktionsdaten integriert, 3D-Visualisierungen und individuelle Berichte erstellt und von Kunden für ihre Zeitersparnis, Genauigkeit und exzellenten Support hoch geschätzt wird.
Yield | PVcase
PVcase Yield is a cloud-based platform that uses Digital Twin technology, advanced ray tracing, and machine learning to automatically generate highly accurate, 3D visualized solar yield projections and customized reports for photovoltaic projects, streamlining site selection, design, and performance estimation while significantly reducing manual input and project development time.
The role of automated solar design software in solar project development
Automated solar design software significantly accelerates the traditionally labor-intensive and error-prone solar project development process by reducing design timelines from hours or days to minutes, enhancing precision, minimizing human error, and ultimately improving efficiency and profitability in the solar industry.
Revolutionizing Solar Energy Modeling with Advanced Technology
PVcase Yield revolutionizes solar energy modeling by integrating detailed 3D digital twins with physics-based energy yield estimation, overcoming the inaccuracies and inefficiencies of conventional simplified models and manual adjustments, thereby enhancing the precision and reliability of photovoltaic power plant design and performance predictions.