Overview
As solar portfolios expand across geographically distributed sites, operators can’t solely rely on constant field visits. However, they need a real-time view of asset health, environmental conditions, and performance. A digital twin solutions provider is solving this problem. They require a rugged computing-capable computer which is able to process on-site data, accelerate AI analytics, support visualization, and enable remote system management. Premio’s rugged GPU accelerated AI edge inference computer provides the solution for this demand.
Challenges
- High-performance CPU was required to continuously handle operational and environmental data for real time digital twin workloads
- GPU acceleration was needed to support AI analytics and responsive digital twin visualization at the edge
- Comprehensive industrial connectivity was necessary to integrate solar equipment, sensors, site networks, and connected devices
- Remote management capabilities were required to monitor, troubleshoot, and recover distributed edge computers while reducing unnecessary site visits
- Fast local storage and rugged 24/7 operation with UL certified hardware were required for demanding solar farm environments
Solution
- Premio’s AI Edge Inference Computer: RCO-6000-RPL-4N-1E
- Intel® Core™ Series 2 processing with up to 96GB DDR5 memory for data intensive workloads
- Dedicated NVIDIA RTX PRO™ 4000 Blackwell SFF Edition GPU support through an EDGEBoost Node
- Comprehensive industrial I/O with EDGEBoost expansion and Out of Band remote management support
- High speed NVMe and SATA storage, wide range DC power, MIL-STD-810H test method compliance, and UL certification
Benefits
- Deployment ready hardware with NDAA and TAA compliance across defense, government, and regulated industries
- Responsive engineering and technical support from Premio's Los Angeles headquarters, ensuring faster communication and deployment assistance
Company Overview
A digital twin technology provider develops solutions that help solar energy operators remotely understand and manage distributed renewable energy assets. Its platform combines real time operational and environmental data with digital representations of physical sites, giving operators greater visibility into equipment health, site conditions, and performance. Continued development is focused on expanding intelligent monitoring, edge analytics, and scalable remote operations as solar infrastructure grows across increasingly distributed locations.
The Challenges
- Continuous Edge Processing
Digital twins require constant real-time processing of operational and environmental data. The customer needed a high-performance CPU platform to handle data processing, application logic, and digital twin workloads with minimal latency.
- GPU Accelerated Analytics
AI analytics and visualization require dedicated GPU performance. The platform needed a professional-grade GPU for local AI inference and detailed visualization while maintaining rugged field reliability.
- Connecting Diverse Solar Assets
Solar farms use devices with many different communication interfaces. The customer needed Ethernet, USB, serial, digital I/O, CAN, and expansion options in one platform to simplify integration and reduce external adapters.
- Managing Distributed Edge Systems
Remote solar sites can be difficult and costly to service. Out-of-Band management was needed for remote monitoring, troubleshooting, and system recovery even when the operating system was unresponsive.
- Storage and Field Reliability
Digital twins generate large amounts of data while operating in demanding environments. The customer needed high-speed removable storage, rugged construction, wide-range power support, and UL-certified hardware for reliable operation.
The Solution
- Rugged AI Edge Inference Computer
Premio’s AI Edge Inference Computer, the RCO-6000-RPL-4N-1E, was selected as the edge computing foundation for the digital twin deployment. Installed at distributed solar sites, the system consolidates local processing, GPU acceleration, device connectivity, data storage, and remote management within a rugged industrial platform. Its architecture allows the digital twin application to remain close to the solar assets generating data while reducing dependence on continuous cloud connectivity.
The RCO-6000-RPL was selected to provide the computing foundation for the digital twin deployment across distributed solar sites. Built on the Intel® R680 chipset with support for Intel® Core™ 12th, 13th, and 14th Gen processors as well as Series 2 processors, the platform provides the computing performance needed to process site data locally. DDR5 memory and PCIe Gen 4 support provide high-speed data handling and expansion capabilities, while triple-display support, ranging from 5K up to 8K, enables advanced visualization of operational data and digital twin environments. The system also supports EDGEBoost technologies, with dual EDGEBoost I/O brackets providing flexible connectivity and EDGEBoost Nodes enabling GPU acceleration, PCIe expansion, and high-speed storage for more demanding workloads. Designed to withstand extreme industrial environments, its rugged, fanless construction, wide-range power input, MIL-STD-810H-based shock and vibration resistance, and UL safety certification support reliable field deployment. Together, these computing, visualization, expansion, and industrial-grade capabilities enable the RCO-6000-RPL-4N-1E to keep critical digital twin workloads close to the solar assets while reducing reliance on continuous cloud connectivity.
- High-Performance CPU and Memory
Intel® Core™ Series 2 processors based on Bartlett Lake S with the hybrid-core architecture provide the CPU performance required to continuously process incoming site and environmental information. Two DDR5 SODIMM slots supporting up to 96GB of memory provide additional capacity and bandwidth for simultaneous data processing, digital twin software, communications, and analytics tasks. This combination enables the edge computer to handle demanding workloads locally while supporting responsive interaction with the digital representation of each solar site.
- Dedicated GPU Acceleration
An EDGEBoost Node supports a dedicated NVIDIA RTX PRO™ 4000 Blackwell SFF Edition GPU, delivering up to 770 AI TOPS to accelerate AI and visualization workloads. At the solar site, the GPU can process inference workloads and visualization tasks without sending every operation to centralized cloud infrastructure. Dedicated acceleration gives the digital twin platform additional computational headroom for increasingly sophisticated analytics as monitoring requirements evolve.
- Industrial Connectivity and Remote Management
Onboard connectivity includes dual 2.5GbE LAN, USB 3.2 Gen 2, serial COM, isolated digital I/O, and CAN interfaces for connecting equipment and supporting site communications. EDGEBoost I/O technology adds modular expansion options for additional networking and connectivity while also enabling Out of Band remote management capabilities. For geographically distributed installations, this allows technical teams to monitor and recover systems remotely and helps reduce avoidable service trips to individual solar farms.
- High Speed Storage and Rugged Operation
The EDGEBoost Node includes a removable four bay U.2 NVMe storage canister with hot swappable NVMe SSD support and RAID 0, 1, and 5 options for high-speed local data storage. An internal and hot swappable 2.5-inch SATA drive bay provides additional flexibility, while passive cooling, a minus 25°C to 45°C operating range with GPU, and 9 to 48VDC input support field deployment. Shock and vibration resistance designed around MIL-STD-810H test methods, together with UL safety certification, further strengthens the platform for continuous operation at distributed solar sites.
The Benefits
- Easier Deployment
NDAA and TAA compliant hardware can help satisfy procurement requirements for regulated projects
- Responsive Engineering Support
Backed by Premio's Los Angeles headquarters, our engineering team provides responsive support to help accelerate system configuration, integration, and deployment.
Conclusion
By moving high-performance processing, GPU accelerated analytics, storage, connectivity, and remote management directly to the solar site; the digital twin provider can deliver greater visibility into geographically distributed renewable energy assets. The rugged edge platform helps maintain responsive digital twin operations while reducing the service burden associated with managing computers across remote locations. As solar deployments continue to scale, the architecture provides a flexible foundation for increasingly intelligent and data intensive monitoring applications.
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