Data Governance
Automated Intelligence Platform for DER Asset Owners
Distributed Energy Management Platform
End-to-End Architecture: From Asset Ingestion to AI-Powered API
Data Sources
🔋
Battery Systems
Real-time metrics
💨
Wind Arrays
Turbine data
☀️
Solar Arrays
Panel telemetry
↓
Ingestion & ETL Layer
💾
Raw Data Extraction
SQL/API queries
🔄
ETL Pipeline
Data transformation
⚙️
Feature Engineering
ML preparation
↓
AI & Modeling
🧠
ML Models
Forecasting & optimization
🤖
AI Agents
Autonomous decision-making
↓
Storage Layer
🗄️
Model Output Store
Predictions & insights only
↓
Access Layer
🌐
API Gateway
RESTful & GraphQL endpoints
Primary Use Case
The core need for large DER asset owners is an automated pipeline that eliminates manual data processing for analytics usage, ensures model freshness, and provides production-grade APIs—all while maintaining audit compliance and minimizing storage costs. In today's energy landscape, data is the most valuable asset. However, it's often trapped in disparate systems, requiring manual, error-prone processes to make it usable. This manual effort is not only a drain on resources but also a significant barrier to realizing the full potential of AI. A robust, automated data platform is the foundation upon which true AI-driven insights are built. It allows businesses to move from reactive analysis to proactive, automated decision-making, enabling them to efficiently implement the advice the AI provides and unlock new revenue opportunities.
How It Powers Energy Independence
- Zero-Duplication Data Pipeline: A nightly cleaner lambda function pulls only the 24-hour delta from your inverters or company data lake. This new data is processed using an adaptive, CPU-only interpolation method and appended to a single, versioned Parquet dataset. The raw historian is never copied, and the cleaned data is up to 8 times smaller than the original, significantly reducing storage costs.
- Model Registry & Feature API: When model drift exceeds a predefined threshold, models are automatically retrained, and new artifacts are pushed to storage. All model training metrics are recorded, and metadata is saved to a records table, providing a complete audit trail. The feature API serves these feature streams to both AI agents and user-facing front-end applications.
- Production-Grade APIs: Secure, rate-limited API providing access to AI agent insights, predictions, and control commands for external applications, enabling seamless integration with existing systems.
Key Features: Zero-duplication data pipeline, automated model retraining, comprehensive audit trail, production-grade APIs, and up to 8x storage cost reduction
Ona Energy Management Platform
Intelligent Operations & Maintenance for Solar Assets
Primary Use Case
Deployed as a cloud-native SaaS platform for solar farm operators, independent power producers, and renewable energy asset managers. Integrates directly with existing SCADA systems, inverters, and weather stations to provide real-time monitoring, predictive maintenance alerts, and automated optimization recommendations for solar installations ranging from 1MW to 500MW+.
How It Powers Energy Independence
- Predictive Maintenance: AI algorithms analyze equipment performance patterns to predict failures before they occur, reducing downtime and extending asset life
- Performance Optimization: Real-time monitoring identifies underperforming panels and automatically adjusts inverter settings to maximize energy output
- Weather-Adaptive Intelligence: Machine learning models trained on African weather patterns optimize energy production for local conditions
Key Features: Real-time dashboards, automated alerts, performance analytics, and integration with existing SCADA systems
Energy Analyst LLM
Municipal Energy Policy Oracle
Primary Use Case
Deployed as a web-based AI assistant for municipal energy departments, city councils, and local government officials. Provides instant access to SSEG regulations, automated application processing workflows, and citizen query handling. Designed for municipalities with limited technical resources who need to manage distributed energy applications efficiently.
How It Powers Energy Independence
- Automated Application Processing: AI reviews SSEG applications for compliance, identifies missing documentation, and provides instant feedback to applicants
- Regulatory Knowledge Base: Maintains up-to-date database of energy regulations, tariffs, and policy changes across all municipalities
- Citizen Self-Service: Residents can get instant answers about solar installation requirements, permits, and grid connection procedures
Key Features: Natural language processing, automated compliance checking, citizen query interface, and municipal dashboard
Ona Edge
Edge AI Processing Network
Primary Use Case
The Edge Layer is the foundation of the Ona Platform's distributed intelligence architecture. It runs on low-power compute devices attached directly to energy assets, enabling real-time forecasting and fault prediction at the source. This layer ensures operational continuity even when connectivity to the central control layer is interrupted. The Edge Layer hosts Predictive AI models for short-term forecasting (0-48 hours), performs local model execution on each node, stores 48 hours of data locally for resilience, operates independently during connectivity loss, and provides real-time predictions for each asset node.
How It Powers Energy Independence
- Local Data Processing: AI workloads run on African soil, ensuring data sovereignty and compliance with local data protection laws
- Low-Latency Computing: Edge nodes provide faster processing than distant cloud services, critical for real-time energy management applications
- Sustainable Infrastructure: Solar-powered nodes reduce operational costs and environmental impact while providing reliable 24/7 compute capacity
Key Features: NVIDIA Jetson Orin Nano modules, solar power integration, low-latency networking, and sovereign data handling
