Metric Collection

Mirox gathers performance data from your renewable energy plants into a single, standardized metric vocabulary — the foundation for every monitoring, analysis, reporting, and forecasting capability on the platform. This page is the reference catalog of what is measured and how the measurements are organized.

The full taxonomy spans 376 standardized metric names across eleven families: plant production (powerplant), battery storage, weather inputs, weather-derived models, and several operational families that describe the collection itself — network monitoring, the agents, the operator fleet, basic network reachability, and the scraper sources. The tables below document the families you will chart most often; the operational families are summarized in the Network Metrics and Agent Metrics sections.

Data Scraper Architecture

At the core of metric collection is the Data Scraper, the edge component that collects time-series data from your plants. Rather than a single generic protocol engine, it uses a dedicated adapter per device family (data loggers, inverters, meters, battery systems), each built for that vendor's interface. The Data Scraper can operate in two deployment modes:

Cloud-Based Collection

When deployed in the cloud:

• Connects to solar parks via secure internet connections
• Aggregates data from multiple sites centrally
• Handles connection management and retry logic
• Optimizes bandwidth usage through intelligent polling

Edge-Based Collection

When deployed at the edge (on-site):

• Directly interfaces with local equipment for minimum latency
• Operates with local buffering to handle internet outages
• Performs preliminary data validation and filtering
• Synchronizes with the cloud when connectivity is available

The dual-mode architecture ensures reliable data collection regardless of site connectivity conditions or system scale.

Collected Metrics

The metric collection system gathers a comprehensive set of measurements that provide complete visibility into renewable energy plant performance. Metrics are grouped along three orthogonal axes:

• Site Metrics describe the physical site itself — its powerplant, its battery storage, and the weather around it.
• Network Metrics describe the connectivity between the site and the cloud.
• Agent Metrics describe the data-collection agents themselves — the scraper sources and the supporting infrastructure services running on the IoT device.

Site Metrics

Site metrics are the raw, time-series measurements collected directly from the equipment installed at a renewable energy site. They are partitioned by domain into the powerplant (PV inverters, grid connection, etc.), the battery energy storage system (BESS), and the weather (forecast and modelled).

Powerplant Metrics

Grid Metrics

Inverter Metrics

Junction Box (GAK) Metrics

String Metrics

Control Metrics

Irradiance Metrics

Environmental Metrics

Loss Metrics

Report Metrics

Digital Twin Metrics

Battery Metrics

The battery_* family is the vendor-independent metric vocabulary for any Battery Energy Storage System (BESS). It follows a strict component hierarchy where instance identification lives in labels, not in the metric name:

battery_environment_*       ambient sensors around the BESS
battery_box_*               a BESS box / container — top of the battery side
└── battery_storage_*       a cabinet / rack inside the box
    └── battery_module_*    a module inside the cabinet
        └── battery_cell_*  an individual cell inside the module

This is a collection-only spec — adapters publish raw values exactly as the vendor exposes them, with no aggregations, derivations, _min / _max / _mean modifiers, or status / health flags. All interpretation, roll-ups and alarming are downstream concerns of the digital twin.

Environment

Ambient sensors around the BESS — distinguished by the sensor label.

Box Level

The top of the battery side — a BESS box / container. Required label: box. Optional labels: inverter, phase. AC-side metrics are only present when the AC↔DC conversion physically sits at box level.

AC-side energy flow

DC-side interface

Chemistry & state

Storage Level

A cabinet / rack inside a box. Required labels: box, storage. AC metrics are rare at this level.

DC-side interface

Chemistry & state

Module Level

A module inside a cabinet — the smallest unit that has its own BMS chip. Required labels: box, storage, module. AC metrics are practically never present here.

DC-side interface

Chemistry & state

Cell Level

The smallest physical unit. Cells are series-connected within a module and share the module current, so per-cell current and power are not meaningful. Required labels: box, storage, module, cell.

Weather Forecast Metrics

Daily Weather Metrics

15-Minute Weather Metrics

Weather Calculation Metrics

Weather Model Metrics

Weather Model Power Metrics

Clearsky Model Power Metrics

Network Metrics

Network metrics describe the connectivity between the IoT device and the cloud, independent of any specific scraper or service.

Agent Metrics

Agent metrics describe the data-collection agents themselves — both the per-source state of individual scraper adapters, and the overall health and traffic of the agents and supporting connectivity. These metrics let your dashboards correlate data gaps with adapter or connectivity failures rather than with the equipment under measurement.

Scraper Source Metrics

The Data Scraper emits one set of these metrics per active adapter source. Series are distinguished by the source, source_num and name labels, so that data gaps can be correlated directly with adapter health rather than with the equipment being measured.

• scraper_health_state: Current adapter state, encoded as an integer: 0 = initializing, 1 = healthy, 2 = unhealthy, 3 = reconnecting, 4 = frozen (the logger is returning stale/stuck values), 5 = paused.
• scraper_cycles_total: Number of fetch cycles the adapter has executed since process start (resets on restart).
• scraper_errors_total: Number of fetch cycles that ended in an exception since process start (resets on restart).
• scraper_last_success_timestamp: Unix epoch seconds of the last successful fetch cycle. Only emitted after the first success.

Agent Service Metrics

These metrics track the health and network usage of the individual agent services (Data Scraper, Digital Twin, and supporting services) running on a device, so you can see whether a data gap is the equipment or the agent itself.

Network Traffic

Agent Health

Organization VPN

Connectivity and traffic for the organization-wide VPN link between the plant and the cloud.

Direct VPN

Connectivity and traffic for each direct (per-plant) VPN tunnel. Series carry a vpn_name label so each tunnel is identified individually.

Operator Metrics

The operator fleet pushes infrastructure metrics for VPN tunnel monitoring and operator health, letting you see the state of the cloud-side connectivity that links your plants to the platform.

VPN Tunnel Metrics

• operator_vpn_connected: 1 if the VPN tunnel has a recent handshake (within the last 3 minutes), 0 otherwise
• operator_vpn_rx_bytes: Total bytes received through the VPN tunnel (counter)
• operator_vpn_tx_bytes: Total bytes transmitted through the VPN tunnel (counter)

Operator Health Metrics

• operator_health: 1 if the operator has an active cloud connection, 0 if in timeout state
• operator_uptime_seconds: Seconds since the operator process started
• operator_cloud_response_age_seconds: Seconds since the last successful cloud API response

Collection Process

The data collection process follows a structured workflow:

1. Discovery: The system identifies all components to monitor based on park configuration
2. Scheduling: Collection schedules are optimized based on metric importance and variability
3. Retrieval: The data scraper connects to devices and collects raw measurements
4. Validation: Collected data is validated for accuracy and range compliance
5. Enrichment: Contextual information is added (timestamps, component identifiers, etc.)
6. Transmission: Data is securely transmitted to the Time-Series Database
7. Verification: The system confirms successful storage and availability

Integration Points

The metric collection system integrates with other platform components:

• Monitoring Dashboard: Provides real-time data visualization
• Event Detection: Supplies data for anomaly and issue identification
• Reporting Engine: Furnishes measurements for performance reports
• Analytics System: Feeds data into advanced performance calculations
• Forecasting Module: Provides historical data for prediction models

Data Security

All collected metrics are protected through multiple security measures:

• Encryption: Data is encrypted both in transit and at rest
• Access Control: Metrics are subject to the same permission model as other resources
• Auditing: All access to metric data is logged for compliance purposes
• Anonymization: Aggregated data used for benchmarking is anonymized

Extensibility

The metric collection system is designed for extensibility:

• New Device Families: We build a dedicated adapter for each device family on request — if your plant uses a logger, inverter, meter, or battery system we do not yet support, it can be added without a generic catch-all.
• Custom Metrics: Support for site-specific or equipment-specific measurements.
• External Data Sources: Weather and reference-irradiance series are pulled in alongside your plant data to power analysis and forecasting.

Related Features

• Data Scraper — the edge agent that collects these metrics with a dedicated adapter per device family
• Digital Twin — turns the raw metrics into simulated production, performance ratio, and loss findings
• Metrics Export API — query and download these metrics for your own use
• MiroxQL — the query format for programmatic and raw access to your collected metrics
• Real-Time Monitoring — live dashboards built on the collected metrics