AMI data arriving in volume you can't process fast enough to act on -- outages identified by customer calls before the NOC sees them?
Grid planning still based on peak-year historical data because there's no platform to analyse the interval data the smart meters are already sending?
Smart Grid Analytics Software Development
AMI deployments generate millions of interval reads and events every day. The value is in processing that data fast enough to act on it -- outage extent mapped before customers call, voltage issues flagged before equipment fails, planning studies built from actual interval data rather than peak-year estimates.
We build custom smart grid analytics platforms for utilities and grid operators. From AMI data ingestion to feeder reliability dashboards, we turn meter data into grid intelligence.
AMI and smart meter data ingestion and processing at scale
Outage detection and localisation from meter event data
Grid performance dashboards for operations and planning
Voltage and power quality analytics
Smart grid analytics software processes the high-volume interval and event data from AMI deployments to give utilities and grid operators actionable information -- outage detection from last-gasp events, voltage quality analysis by feeder, load forecasting for planning, and reliability metrics calculated automatically. RaftLabs builds custom smart grid analytics platforms that handle AMI data at scale and integrate with your existing SCADA and OMS systems.
100+Products shipped
·24+Industries served
·FixedCost delivery
·12-14Week delivery cycles
AMI data without analytics is just storage cost
A smart meter rollout generates value only when the data it produces is processed fast enough to inform decisions. Last-gasp events are useless if they arrive in the analytics platform after the crew is already on site. Interval data from 500,000 meters has no planning value if it takes two weeks for the data team to run a study from it.
Smart grid analytics infrastructure needs to be designed for the volume and latency requirements of AMI data from the start. Time-series storage, event stream processing, and query optimisation for interval data are different engineering problems from general-purpose analytics. Getting the data model right means the difference between queries that return in seconds and studies that take days to run.
We build analytics platforms that are scoped to your AMI vendor, your grid topology, and your operational workflows -- not generic energy analytics tools that require configuration to fit your environment.
What we build
AMI data pipeline
High-volume meter data ingestion from your AMI head-end system via API, SFTP, or direct database connection. Data validation, gap filling, and quality flagging on ingest. Time-series storage optimised for interval data query patterns -- feeder aggregation, period comparison, and per-meter history retrieval. Support for multiple data streams from the same meter endpoint. Schema designed to handle interval data from millions of endpoints without query performance degrading as data volume grows.
Outage detection and localisation
Last-gasp event processing to detect meter power loss events and map outage extent in near real time. Outage boundary estimation from which meters have reported power loss and which have not. Estimated restoration time calculation based on crew availability and fault location. Integration with your Outage Management System or field crew dispatch platform. Outage dashboards for the NOC showing confirmed outages, estimated customers affected, and restoration status -- without waiting for customer calls to define the scope.
Voltage and power quality monitoring
Voltage deviation detection at meter level from interval voltage readings reported by AMI endpoints. Sustained over-voltage and under-voltage events flagged by feeder and transformer. Power factor analysis and harmonic distortion flagging where AMI endpoints report power quality data. Voltage profile visualisation across a feeder showing the voltage gradient from substation to end of line. The information that identifies voltage regulation issues before they cause equipment damage or customer complaints.
Load forecasting and grid planning
Interval data aggregation by feeder, substation, and zone for planning studies. Coincident peak analysis from actual interval data rather than assumed diversity factors. DER impact modelling for solar and battery installations using measured net consumption profiles. Load growth trend analysis at the feeder level to identify capacity constraints before they emerge. Planning studies that take hours rather than weeks because the data is already structured and queryable.
Distribution analytics dashboard
Feeder loading dashboards showing real-time and historical load against thermal rating. Asset utilisation metrics by transformer and feeder. Reliability metrics calculated automatically from outage event data -- SAIDI, SAIFI, and CAIDI by feeder, zone, and network area. Trend views for reliability performance over rolling periods. Operational dashboards for the NOC and planning dashboards for network engineers built from the same underlying data, filtered to the right level of detail for each audience.
Non-technical loss detection
Consumption pattern analysis to identify anomalies consistent with meter tampering, illegal connections, or metering errors. Energy balance calculations comparing distributed generation and import at the transformer level against downstream meter aggregates. Loss attribution by network segment to focus investigation effort. Meter health flags from read quality indicators and communication failure patterns. The analysis layer that turns AMI data into an NTL detection tool without requiring manual inspection of individual meter records.
Frequently asked questions
Smart grid analytics is the software layer that processes data from AMI deployments, SCADA systems, and grid sensors to give utilities operational and planning intelligence. It handles the data volume and latency requirements that make AMI data difficult to use in general-purpose analytics tools -- millions of interval reads per day, event streams from last-gasp and power quality endpoints, and time-series query patterns that require purpose-built storage. The output is operational dashboards for the NOC, planning data for network engineers, and reliability reporting for regulators -- all from the meter data the network is already generating.
We integrate with AMI head-end systems from Itron, Landis+Gyr, Honeywell Elster, Aclara, and Sensus via their published APIs or data export formats. For SCADA integration, we support DNP3, IEC 61850, and OPC-DA and OPC-UA connections to substation automation systems. Where direct integration is not available, we work with SFTP file delivery or database replication from your existing data historian. We assess your specific AMI vendor and SCADA environment during scoping and design the integration to fit your architecture.
AMI data at scale requires time-series storage -- purpose-built databases like TimescaleDB, InfluxDB, or Apache Parquet-based storage on cloud object stores, depending on query patterns and retention requirements. We design the data pipeline to handle burst ingest at meter read time and continuous event streams from last-gasp and power quality endpoints. Query performance is maintained as data volume grows by partitioning data by time period and network topology and pre-aggregating commonly queried metrics. We size and architect the storage layer based on your meter count, read frequency, and data retention requirements during scoping.
The platform calculates SAIDI (System Average Interruption Duration Index), SAIFI (System Average Interruption Frequency Index), and CAIDI (Customer Average Interruption Duration Index) from outage event data. Metrics are calculated at feeder, zone, substation, and network area level for any selected time period. Where AMI last-gasp data is the outage detection source, the platform also calculates the time from outage start to NOC awareness, which is a metric that matters operationally but is not typically tracked in OMS systems. All metrics are formatted for internal reporting and for regulatory reliability reporting obligations.
