Technical Concepts Overview
This section provides a deeper look into the core technologies and methodologies that power the Ona Intelligence Layer. It is designed for Developers and other technical users who want to understand how our platform works under the hood, including our machine learning models, data processing pipelines, and architectural decisions.
Understanding these technical concepts will help you make better decisions about data preparation, model selection, and integration patterns. Whether you’re optimizing forecast accuracy or building custom integrations, this knowledge will prove invaluable.
Quick Start
To get started with our technical concepts, we recommend beginning with Data Standardization to understand how we process your data, then exploring Machine Learning Models to learn about our forecasting algorithms.
What You Can Find Here
Machine Learning Models
An overview of the different machine learning models we use for forecasting and anomaly detection. Learn about generic models, customer-specific models, and how model selection impacts forecast accuracy. Understand the algorithms, training processes, and performance characteristics of each model type.
Learn More →Data Standardization
A comprehensive look at our automated process for ingesting, cleaning, and standardizing data from a wide variety of sources. Understand how we handle different data formats, time zones, units, and quality issues to ensure consistent inputs for our machine learning models.
Learn More →Asoba Protocol
A performance-collateralized intelligence and settlement layer that binds operational commitments to consequences at the same timescale at which those commitments are made. Learn how the protocol enables coordination under uncertainty, performance-indexed financing, and verifiable guarantees for distributed energy systems.
Learn More →Core Concepts
Our platform is built on several key technical principles:
Machine Learning Architecture
We employ a hybrid approach combining generic pre-trained models with customer-specific fine-tuned models. Generic models provide immediate value for new users, while customer-specific models deliver superior accuracy after training on site-specific historical data.
Data Processing Pipeline
Our data standardization pipeline handles diverse input formats, automatically detecting and correcting common issues like missing timestamps, unit conversions, and timezone discrepancies. This ensures consistent, high-quality data feeds into our forecasting models.
Model Training Process
Customer-specific models are trained using transfer learning techniques, starting from generic models and fine-tuning on customer data. This approach balances accuracy with training efficiency, allowing us to deliver custom models quickly.
Forecast Generation
Forecasts are generated using ensemble methods that combine multiple model predictions, incorporating weather data, historical patterns, and site-specific characteristics to produce accurate predictions.
Asoba Protocol
The Asoba Protocol addresses the coordination constraint in distributed energy systems by introducing a mechanism that binds operational commitments to consequences. It enables performance-collateralized activities, automatic enforcement of commitments, and performance-indexed financing, ensuring that failures are contained and paid for locally rather than propagated through the system.
Popular Topics
These technical concepts are frequently referenced:
- Forecasting Models: Deep dive into ML model architecture
- Data Standardization: How we process and clean your data
- Asoba Protocol: Performance-collateralized intelligence and settlement layer
- Model Training: Upload training data for custom models
- Forecast Accuracy: Factors affecting forecast performance
Next Steps
Now that you understand our technical concepts:
- Learn About Models: Explore Machine Learning Models to understand our forecasting algorithms
- Understand Data Processing: Read Data Standardization to see how we handle your data
- Explore the Protocol: Review the Asoba Protocol to understand how we enable coordination and performance guarantees
- Generate Forecasts: Use the Forecasting Guide to apply this knowledge
- API Integration: Review the API Reference for technical implementation details
See Also
- Forecasting Guides - How to use forecasting features
- API Reference - Technical API documentation
- Developer Guide - Integration patterns and best practices