Optimizing BESS Operations: Real-Time Monitoring & Predictive Maintenance with InfluxDB 3

By Suyash Joshi Developer
Jan 13, 2026

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For IT and OT engineers managing Battery Energy Storage Systems (BESS) and other distributed energy resources (DER), the challenge isn’t just dealing with energy. It’s a data problem, or managing the massive stream of real-time telemetry these systems generate. For example, a BESS site produces a constant stream of time-series data from BMS, PCS, SCADA, EMS, and more, and operating it means ingesting, correlating, and acting on that data in real time. And this challenge changes with scope. At a single site, telemetry drives asset health and safe operation—from cell temperatures to inverter vibration. At fleet scale, the same data supports coordinated operations and incident response across sites. When retained at full resolution, it also enables historical analysis for degradation tracking, predictive maintenance, and long-term optimization.

Data flows from Operational Technology (OT) signals to Information Technology (IT) systems. Most BESS operators already run a slew of disparate systems:

  • BMS answers: Are the batteries safe and healthy?
  • PCS answers: Can I deliver or absorb power?
  • EMS answers: When should I charge or discharge?
  • SCADA answers: What’s happening right now on site?

Individually, these systems work well. The problem starts when you need a unified, time-aligned view across all of them, especially across multiple sites. InfluxDB sits at the center as a shared time series platform, consolidating telemetry from all sources and serving it to operations, analytics, and automation workflows.

One popular architecture: TIG (Telegraf → InfluxDB → Grafana)

A typical pattern for BESS telemetry is the TIG stack, because it cleanly separates collection, storage/query, and visualization and scales from a single site to a fleet.

BESS graphic

Telegraf (Collection + Normalization)

Telegraf acts as a lightweight collection agent at the edge or in your DMZ, with plugins for common OT and IoT protocols (Modbus, OPC-UA, MQTT, SNMP, HTTP). Use it when you want: Fast onboarding of new signals without writing custom collectors Store-and-forward style buffering patterns at the edge (architecture-dependent) A consistent metric format before data hits your central platform

InfluxDB 3 (The Time Series Database)

InfluxDB is where BESS telemetry becomes operationally usable, offering:

  • High-speed ingestion so you don’t drop high-frequency telemetry during bursts (faults, transients, dispatch changes).
  • High-cardinality modeling so you can tag by site/rack/module/cell/inverter.
  • SQL support so IT/data teams can query using familiar tools and patterns (and integrate with BI/analytics stacks).
  • Low-latency “hot path” reads using Last Value Cache and Distinct Value Cache for dashboards that need current state now (SoC, alarms, inverter status, thermal conditions).
  • Scalable: Deploy a single InfluxDB 3 Core or multiple-node Enterprise cluster as per your needs.

Grafana (or Power BI/Apache, SuperSet, etc.)

Grafana turns fast queries into multi-panel dashboards commonly used for:

  • Live SoC/power/dispatch tracking
  • Temperature gradients and thermal risk monitoring
  • Voltage spreads, imbalance indicators, and fault timelines
  • Per-site and fleet rollups with consistent tags

Real-time data processing: anomaly detection for predictive maintenance

Traditionally, predictive maintenance required a complex pipeline: extracting data to a separate Python application server, running analysis, and writing results back. This adds latency, maintenance overhead, and security risks.

InfluxDB 3 Core & Enterprise brings the data processing to where the data lives.

Using the Processing Engine and ready-made plugins, you can perform stream processing within the database infrastructure.

  • Real-Time Detection: As shown in the “Anomaly Detector” toggle in our demo, the system can identify thresholds (e.g., Temp > 80°C or Vibration Drift) in real-time as data arrives.
  • Simplified Architecture: You eliminate the need for an external Python application server or complex stream-processing clusters (such as Kafka or Flink) to detect spikes.
  • Operational Plugins: Beyond anomaly detection, plugins handle tasks like downsampling (converting 10 ms raw data into one-minute averages for long-term storage) and alerting without leaving the platform.

Overview

BESS operations depend on fast, reliable, and time-aligned telemetry. InfluxDB 3 provides a single platform to support real-time monitoring, anomaly detection, and forecasting at fleet scale without adding unnecessary complexity to your data pipeline. If you’re building or operating real-time BESS data systems, you may find our customer case study on ju:niz energy helpful. As always, we’d love to hear your questions/comments or see what you have built on Slack, Discord, and our Community Forum.