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Altair

Demand Supply

Optimisation

Enhance operational efficiency and sustainability through an AI-powered Energy Balance Optimisation System. This solution uses machine learning to forecast demand, predict supply fluctuations from various energy sources, and optimise real-time grid decisions.

It can help the business

  • Minimise energy waste and curtailment
  • Reduce reliance on high-emission, high-cost sources
  • Ensure grid reliability under demand variability
  • Provide actionable insights for pricing and resource dispatch
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Input required

Feature Engineering

Demand Side

  • Historical energy consumption
    (by region, time)
  • Weather forecasts
  • Holiday/working day info
  • Demographic and population data

1

Supply side

  • Real-time and forecasted generation from: → Coal, solar, wind, hydro
  • Cost per kWh by source
  • Grid-level storage capacity & state

2

Infrastructure/Assets

  • Transmission capacity
  • Substation locations
  • Outage data
  • Curtailment rules or constraints

3

Market Signals

  • Real-time pricing data
  • Export/import obligations or opportunities

Benefits

Operational Efficiency

Automated dispatch decisions reduce manual overhead

Environmental Impact

Higher renewable utilisation, lower carbon emissions

Cost Optimisation

Minimised use of expensive power sources and grid penalties

Grid Resilience

Forecast-based decisions improve reliability and load balancing

Scalability

Solution can extend to other use cases like outage prediction or demand response

Predictive

Maintenance

Improves grid reliability and lowers O&M costs by using AI/ML for Predictive Maintenance and Asset Health Monitoring. This approach shifts utilities from reactive to data-driven predictive maintenance, reducing failure risks and optimising field crew deployment.

It can help the business

  • Reduced Unplanned Outages:
    Early detection of asset failure avoids costly blackouts
  • Lower O&M Costs:
    Minimised unnecessary routine maintenance and optimized crew dispatch
  • Asset Life Extension:
    Proactive care improves longevity and utilisation of infrastructure
  • Improved Safety:
    Prevents dangerous equipment failures and reduces on-site risk

Input required

Feature Engineering

Asset Condition Data

  • Transformer temperatures
  • Vibration data
  • Partial discharge signals
  • Oil/gas analysis reports

1

Grid Sensor & IoT Data

  • SCADA signals
  • Smart meter outage alerts
  • Voltage/current fluctuations
  • Load profile anomalies

2

Historical Failure Logs

  • Equipment failure history
  • Repair/replacement logs
  • Maintenance actions taken

3

Environmental Context

  • Weather (heatwaves, humidity, storms)
  • Bushfire risk zones – Flood-prone areas

4

Asset Metadata

  • Asset age, model, installation date
  • Maintenance cycles and warranty info

Input required

Feature Engineering

  • Asset Health Predictor:
    Supervised ML models predict probability of failure for transformers, lines, etc.
  • Fault Pattern Detector:
    Anomaly detection engine using real-time SCADA/IoT data to catch early failure signals
  • Maintenance Optimiser:
    Prioritises assets based on risk, cost, and geographic spread to suggest optimal crew tasks
  • Asset Health Predictor:
    Supervised ML models predict probability of failure for transformers, lines, etc.
  • Reporting & Insights:
    Dashboards for asset managers showing high-risk assets, cost forecasts, and MTBF metrics

Product Suite Altair

ALTAIR SMARTWORKS

Ingest and process IoT, sensor, and SCADA data from field assets

ALTAIR RAPIDMINER

Develop predictive maintenance ML models and anomaly detection algorithms

ALTAIR PANOPTICON

Real-time visual analytics and alerting dashboards for field operations and reliability teams

ALTAIR HYPERSTUDY

Run scenario simulations on maintenance schedules vs cost vs downtime trade-offs

ALTAIR MONARCH

Clean, prepare, and transform historical maintenance logs and asset metadata

Value Proposition

Graph Studio

Unlike flat asset registries or isolated time-series systems, a graph database can represent:

  • Asset-to-asset connectivity (e.g., pole → line → transformer → substation)
  • Spatiotemporal dependencies (weather, bushfire zones, terrain)
  • Failure propagation chains

Node Type

  • Asset (pole, transformer, cable)
  • Inspection
  • Failure Event
  • Weather Zone
  • Maintenance Crew
  • Location

Edge Type

  • CONNECTED_TO, FEEDS, MOUNTED_ON
  • INSPECTED
  • FAILED_AT
  • LOCATED_IN, EXPOSED_TO
  • ASSIGNED_TO, RESPONDED_TO
  • CONTAINS, ADJACENT_TO

Attributes / Metadata

  • Age, specs, health score, RUL
  • Date, result, image, technician
  • Type, date, impact level
  • Fire risk, rainfall, wind
  • Skills, location
  • Risk score, terrain type

Benefits

Reduced Unplanned Outages

Early detection of asset failure avoids costly blackouts

Lower O&M Costs

Minimised unnecessary routine maintenance and optimised crew dispatch

Asset Life Extension

Proactive care improves longevity and utilisation of infrastructure

Improved Safety

Prevents dangerous equipment failures and reduces on-site risk

Data-Driven Decision Making

Clear visibility into asset health drives strategic investments