Graphite Energy Powers Industrial Decarbonization with Real-Time Data from InfluxDB

Overview

Building smarter systems for industrial decarbonization

Graphite Energy develops thermal energy storage systems that turn renewable electricity into heat when industrial processes need it, helping manufacturers reduce fossil fuel use and carbon emissions. Operating those systems depends on continuous data from machines, customer equipment, weather sources, and third-party APIs. Graphite Energy uses InfluxDB to store and analyze that data in real-time, supporting remote operations, engineering, digital twins, and production optimization.

Challenge

Moving from batch analysis to real-time operations

As Graphite Energy’s machines became more instrumented, the amount of operational data grew quickly. Each device can generate roughly one million data points per day, with measurements collected every one to 15 seconds.

Traditional relational databases worked when datasets were smaller, but as the number of signals increased, queries became noticeably slower. At the same time, Graphite Energy was moving away from batch analysis toward real-time monitoring and analytics.

The data itself also presented a challenge. Industrial sensors report at different times and frequencies, while engineers need consistent, time-aligned datasets for analysis. A team might need to examine a full year of machine behavior at hourly intervals, then investigate the last five minutes second by second.

Graphite Energy needed a database built for time series data that could handle growing volumes, make it easy to resample and transform data at query time, and give mechanical, electrical, and process engineers direct access without relying on a dedicated database team.

enter influxdb

A time series architecture from the edge to the cloud

Graphite Energy standardized on Node-RED for data collection and engineering and InfluxDB for time series storage.

At customer sites, industrial controllers collect data from machine systems and use Node-RED to process raw signals before sending them to InfluxDB. A local InfluxDB on each unit instance supports real-time operations, dashboards, and edge computing close to the equipment.

Graphite Energy also uses InfluxDB in its remote operations environment alongside Grafana for dashboards, monitoring, and engineering support. In the cloud, InfluxDB serves as the company’s primary data store, combining machine telemetry with weather data, third-party APIs, and other operational information.

InfluxDB also gives engineers the flexibility to transform data when they query it. Measurements such as pressure, temperature, and flow can be aligned in time and combined into the engineering values teams actually need, rather than requiring every calculation to happen later in a separate toolchain.

That makes it easier for engineers to self-serve operational data and reuse those calculations across dashboards, models, and engineering applications.

Result

From machine data to predictive performance

With InfluxDB at the center of its time series architecture, Graphite Energy has moved much of its analytics from batch processing to real-time operations.

Engineers can work directly with operational data, while Grafana dashboards and alerts provide visibility into machines in the field. That same time series data also powers Graphite Energy’s Python-based digital twin, which models an operating machine to within approximately 5% of actual performance.

The digital twin lets engineers move backward and forward through machine behavior and is becoming an important part of Graphite Energy’s toolkit for production optimization and predictive analysis.

InfluxDB has also given the team confidence that the architecture can keep up as the workload grows.

what’s next

Preparing for 100x more time series

Graphite Energy expects the number of time series it manages to increase by roughly 100x as its machines add more sensors, actuators, and data from surrounding systems.

The company is also moving more intelligence toward the edge, including on-device machine learning and Python-based analytics that can support its digital twin and help identify optimal operating conditions in real time.

As those systems become more data-intensive, Graphite Energy plans to continue using both local and cloud-based InfluxDB deployments as the foundation for its time series architecture.