📄️ Project Structure
Complete .NET solution structure, module responsibilities, and dependency graph for the Ampra platform.
📄️ Constants Reference
Complete catalogue of all constant classes, enumerations, and static configuration values used across the Ampra platform.
📄️ Validators
Complete reference of all FluentValidation validators, their rules, and constraints for every write operation in the Ampra platform.
📄️ MQTT Ingestion Worker
Deep-dive into the standalone MQTT ingestion microservice — connection lifecycle, message processing pipeline, data normalisation, and quality controls.
📄️ Services Reference
Complete reference for every service interface and its implementation in the Ampra platform. Services follow a clean-architecture pattern: interfaces live in Ampra.Application, implementations in Ampra.Infrastructure, and dependency injection is configured in Ampra.Web/Startup.cs.
📄️ Middleware & Pipeline
The Ampra Web API uses a carefully ordered middleware pipeline and action filters to handle cross-cutting concerns: CORS, exception handling, authentication, authorization, and request validation.
📄️ ML Prediction Service
The Ampra ML service is a standalone Python microservice that trains per-source XGBoost models and generates 7-day, 30-minute-resolution forecasts for solar power, load, battery state of charge, and battery voltage. It uses a physics-aware hybrid approach that blends machine learning predictions with solar physics and historical profiles.