Energy prices across Europe have surged in recent years, putting additional pressure on manufacturers and making European industry less competitive on the global stage. At the same time, most factories still lack the kind of structured, process-level energy data needed to control costs with precision. In this article, we share Fabrity’s practical approach to industrial energy data acquisition—how we build on existing infrastructure, close measurement gaps with minimal disruption, and turn raw readings into process-level insight using the Nexen Suite.
Energy data acquisition in brownfield factories: What we see in the field
At Fabrity, most of the factories we work with are brownfield environments, and energy measurement is one of the most common starting points for Industry 4.0 initiatives. Power meters and analyzers already deliver valuable measurements. However, without a proper data acquisition and processing layer, these readings rarely translate into reliable, meaningful analysis. Collection is often partial, automation limited, and measurements disconnected from how production actually consumes energy.
Energy data very often exists in silos. Individual machines, switchboards, or lines may be metered, but only for local visibility—through panels, stand-alone computers, or vendor-specific interfaces. Proprietary protocols make it difficult to consolidate this data into a single, consistent view, leaving energy information fragmented and underused.
Manual data collection remains surprisingly common. Energy readings are taken from displays once per shift or per day and written down on paper or entered into spreadsheets. This approach reduces continuous consumption monitoring to isolated snapshots, obscuring peak loads, transient events, and the real relationship between energy use and production activity.
The core issue lies in how traditional energy infrastructure was designed. Meters are placed according to electrical distribution logic—switchboards, feeders, and circuits—rather than according to production processes. Built for reliability and safety, not analytics, this setup makes it difficult or impossible to attribute energy consumption to specific assets or process stages. Physical constraints only reinforce the problem: Large sites, limited cabling, and active production areas make expanding wired measurement infrastructure expensive and disruptive.
The gap becomes evident when business questions arise: How much energy does this process consume, stage by stage? In most brownfield factories, the existing energy measurement setup cannot answer. At Fabrity, addressing this mismatch is our bread and butter. Below, we show how our practical, field-tested approach transforms fragmented energy data into process-level insights.
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Granular measurement close to the process (why sub-metering matters)
We aim to measure energy as close to the production process as possible, because granularity is what turns raw readings into insight. Low-level measurement enables visualization of power curves over time, exposing load profiles, peaks, and process-related patterns that aggregated data cannot reveal. While in theory energy architectures should avoid sub-metering, in practice brownfield environments always create meter hierarchies. The key is not avoiding hierarchy but designing it deliberately so that detailed measurements still roll up into accurate, reliable totals.
Virtual meters: Calculating unmeasured consumption from existing readings
In brownfield factories, not every consumption point can be measured physically. Space constraints, technical limitations, or installation costs often make additional meters impractical or impossible to install. To address this, we use virtual meters—calculated values derived from existing measurements. A typical example is subtracting the readings of two sub-meters from a parent meter to obtain the consumption of an unmeasured area or process. This approach allows us to preserve a clear measurement hierarchy and maintain full energy accountability through consistent chains of sums and differences, even where physical measurement is not possible.
One telemetry layer in Nexen: A tag-based data model for consistent analytics
All energy and utility data collected by Nexen is stored in a single, centralized telemetry database built around tags rather than devices. This approach allows data from multiple sources—directly from meters and sensors or indirectly through existing customer systems—to be unified in one consistent structure. By centralizing measurement data in a common telemetry layer, we create a reliable foundation for analysis, visualization, and cross-process comparison, regardless of how or where the data was originally acquired.
Energy forecasting with Nexen Predict: From monitoring to proactive planning
Nexen Predict extends energy monitoring into forward-looking planning by forecasting energy consumption based on historical telemetry and production patterns. Instead of reacting to yesterday’s numbers, teams can anticipate upcoming peaks, identify unusual demand trends early, and prepare corrective actions before costs escalate. This makes production scheduling and energy planning more data-driven, especially in plants where loads depend heavily on the product mix and operating regimes. With Nexen Predict, energy becomes a variable you can manage proactively—not just report after the fact.
Deploying Nexen Suite on-premises or in the cloud
Nexen is designed to operate in different IT/OT environments. In the most restrictive scenarios, it can run fully on-premises on an industrial-grade server, with no Internet access and all data processed within the local network. Alternatively, for organizations that prefer cloud-based architectures, Nexen can be deployed in the customer’s cloud environment, such as Azure. Some clients choose the cloud, others do not, and our approach supports both models without compromising functionality or security.
Nexen Suite for energy measurements—benefits for the clients
- Energy intensity as a core KPI—quantify the true cost of production by calculating energy consumption per unit or per ton of product.
- Process-level transparency—break down energy usage by process, production line, or individual stage to pinpoint losses and prioritize improvements.
- One integrated energy view—consolidate data from all meters and systems into a single analytical layer, eliminating silos and manual reconciliation.
- Actionable analytics and visualization—move beyond raw readings with dashboards, trends, and comparisons that teams can act on.
- Predictive insight with Nexen Predict—forecast energy prices using telemetry-driven models to plan production and purchasing more confidently.
Fabrity’s approach: From energy distribution to process-level measurement
Every optimization starts with measurement. From our perspective, the ultimate goal of energy data acquisition is simple to state but difficult to achieve: to understand energy consumption per asset and per production process. Before optimization, benchmarking, or advanced analytics can begin, energy must be measured accurately, consistently, and in a way that reflects how production actually works.
On-site assessment: How we verify the existing metering and identify gaps
Every energy management project on brownfield begins on the shop floor. Before we connect a single device or collect a single data point, we carry out an on-site assessment to understand how energy is actually distributed and consumed. Together with the plant team and our local partners, we locate existing meters, verify installations, and identify where measurements already exist and where they are missing. This step is critical for checking technical details such as current transformers, wiring constraints, and installation feasibility. Without this ground-level understanding, even the best-designed energy architecture remains theoretical.
Reusing existing energy meters and closing gaps with minimal disruption
Our starting point is always the existing energy measurement infrastructure. In brownfield factories, we avoid disrupting what is already in place and focus on reusing existing meters and analyzers wherever possible. We collect data both from meters that are already installed and, where necessary, from additional meters we deploy to close visibility gaps. Energy data is typically acquired using Modbus RTU connected to a gateway that aggregates measurements and forwards them to the Nexen Suite—our proprietary IoT platform, where they can be visualized, analyzed, and correlated with production data. In areas where cable infrastructure is not feasible or would be too disruptive to install, we extend measurement coverage using LoRaWAN-based devices, ensuring consistent data acquisition even in hard-to-reach parts of the facility.
Mapping meters to production stages to calculate energy cost by process
Measurement only becomes valuable when it is tied to how production actually runs, which is why we map the facility’s production processes as part of every Nexen deployment. We align meters, assets, and virtual calculations with real process flows—lines, operations, and stages—so energy consumption can be attributed to the right step rather than the nearest electrical cabinet. This process-oriented mapping allows Nexen to calculate energy cost per asset, per process, and per production stage, giving manufacturers a clear view of where energy is creating value, where it is being lost, and which stages should be prioritized for optimization.
Noninvasive integration: Collecting data without touching OT networks
We integrate energy data into Nexen Suite using a deliberately noninvasive communication approach. Data acquisition runs alongside existing infrastructure and does not interfere with the OT network or production logic. For example, data can be transmitted using lightweight protocols such as MQTT, or alternatively via Modbus TCP but in the separated Wi-fi network without requiring access to controllers or PLCs. By keeping Nexen outside the OT network while still close to the data, we ensure secure, reliable energy visibility without increasing operational or cybersecurity risk.
Energy is only the starting point
Electricity is usually the easiest resource to measure, which is why most data initiatives begin there. But meaningful process insight requires visibility into other utilities as well—gas, compressed air, nitrogen, or water. These resources often play a critical role in production costs, yet they are far more challenging to instrument, especially in brownfield environments.
Gas is a good example. Many factories still operate older gas meters that were never designed for digital integration. Replacing them outright is often expensive and carries the risk of production downtime. Instead, we take a pragmatic approach. Where possible, we use already installed gas meters with pulse outputs or add converters to existing meters, allowing data to be collected without disrupting supply. Technologies such as M-Bus, combined with M-Bus–to–Modbus converters, enable integration into the same telemetry layer as energy data. In locations where cabling is impractical, LoRaWAN devices provide an effective alternative.
By applying the same noninvasive principles used for energy measurement, Nexen extends telemetry beyond electricity, bringing multiple utilities into a single, process-oriented analytical view.
The next step: Start small, scale fast
You don’t need a multi-year transformation program to get value from energy data. The fastest path is to begin with existing meters, connect them into a single telemetry layer, and expand only where visibility gaps block analysis. Fabrity’s Nexen Suite is designed for exactly that journey—from first measurements to process-level KPIs and forecasting. If you’re planning your next energy initiative, we can help you define the scope, map processes, and build a rollout plan that delivers results early.
Drop us a line at sales@fabrity.pl to see how we can help.
