Energy systems generate high-frequency telemetry from geographically distributed assets in heterogeneous formats - including DNP3 for substations, Modbus for renewables, and OPC UA for grid infrastructure. KŌJŌ Stack normalizes data at ingestion, filters signals at the edge, and ensures reliable delivery to analytics and control systems. Energy systems require structured, reliable data at the source to operate efficiently.
Structured at the Source
Generators, substations, renewables, and grid equipment each produce telemetry in different formats with different timing models. No common structure exists across asset types, making fleet-wide analysis impossible without extensive per-source normalization.
Energy assets are spread across wide areas connected by intermittent, bandwidth-constrained links. Telemetry must survive connectivity outages without data loss-yet most architectures treat network reliability as a given.
High-frequency sensors produce enormous volumes of telemetry where the vast majority represents no meaningful state change. Without filtering at the source, analytics and optimization systems are overwhelmed with noise rather than signal.
Without structured, prepared data at the first mile, downstream systems inherit every inconsistency, gap, and limitation of the raw source data.
When each asset type reports telemetry in vendor-specific formats with incompatible addressing, constructing a fleet-wide view requires per-source normalization in every analytics pipeline. Cross-asset correlation-comparing turbine performance against weather, grid demand, or maintenance schedules-requires manual data reconciliation.
Energy assets connected by satellite, cellular, or intermittent WAN links lose data during outages without any indication to downstream systems. Analytics and optimization models operate on datasets with invisible gaps, producing results that appear valid but are based on incomplete records.
High-frequency sensors on generation and distribution equipment produce enormous telemetry volumes where the vast majority represents no meaningful state change. Without filtering at the source, optimization and forecasting systems are overwhelmed with noise, increasing compute costs and degrading model accuracy.
Every asset-regardless of type, location, or data format-publishes to the same ISA-95 compliant namespace. Telemetry from generation, transmission, and distribution follows identical structure, enabling fleet-wide queries and cross-asset correlation.
Report-by-Exception with configurable deadband thresholds filters insignificant changes at the point of ingestion. Only meaningful state transitions leave the site. CEL expressions compute derived values and composite signals at the edge, reducing transmitted volume by an order of magnitude.
Durable local buffering persists telemetry before acknowledgment. During connectivity outages, data accumulates locally with configurable retention. On reconnection, buffered data replays in order with original timestamps. Durable buffering and ordered replay maintain data continuity across any network condition.
IEEE 1815-2012 master station with polling and unsolicited (outstation-pushed event) modes. TCP, serial, and TLS transports with per-endpoint reconnect backoff and automatic outstation-restart recovery. Purpose-built for substations, RTUs, and utility-grade SCADA infrastructure.
Event-driven pipelines execute with bounded latency and predictable ordering. Normalized, filtered telemetry reaches analytics and optimization systems with consistent timing and structure-enabling deterministic decision-making across the fleet.
Energy and utility environments present a unique combination of protocol heterogeneity, geographic distribution, and network constraint. A single utility may operate substations reporting via DNP3, wind turbines communicating over Modbus TCP, solar inverters using SunSpec over Modbus, and grid sensors on OPC UA-all connected by links ranging from fiber to satellite. Without edge-level normalization, each telemetry stream arrives at central systems in a different format with different timing characteristics. Temporal alignment across assets is impossible without a shared time base established at ingestion. Durable buffering with ordered replay is not optional-it is a prerequisite for any analytics that depends on complete time-series records. This architectural requirement can only be met when data is structured and buffered at the first mile, at each asset location.
Standard structure across all distributed assets
Edge filtering transmits only meaningful signals
Buffering and replay over constrained and intermittent links
“Telemetry from hundreds of distributed assets now follows a single canonical structure. Edge filtering reduced our transmission volume dramatically, and we haven't lost a single record-even over satellite links.”
Owning the first mile ensures energy & utilities data is consistent, contextualized, and usable across the enterprise.
