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  • Track and monitor growing number of supply, demand, and storage nodes on grid to improve overall reliability and cost of service.
  • Optimize maintenance performance through learning models that account for effectiveness of manual intervention actions.
  • Optimize grid performance in context of variable renewable supply sources, storage assets, demand response and load curve forecasting.
  • Track grid asset performance and improve reliability with proactive performance monitoring.
  • Mitigate grid equipment failure and related outage events through near-term automated alerts.
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Sectors Plutoshift supports:

power generation industry icon

Power
Generation

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Distributed
Power

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Battery
Storage

wastewater management industry icon

Wastewater
Treatment

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Grid
Operations

Use Cases

1.
Power generation water treatment
2.
Micro-grid efficiency
3.
Vegetation management
4.
Reliability management
1.
Apply predictive algorithms to equipment and processes to spot maintenance needs and develop optimal maintenance strategies
2.
Optimize power supply and storage assets to improve micro-grid performance
3.
Combine internal and external data sources (eg, satellite imagery, drone-data, precipitation/moisture) to prioritize field activities in context of cost and risk.
4.
Minimize unplanned outages at lower cost of service through predictive maintenance programs.

Use Cases

Power generation water treatment.
Apply predictive algorithms to equipment and processes to spot maintenance needs and develop optimal maintenance strategies
Micro-grid efficiency.
Optimize power supply and storage assets to improve micro-grid performance
Vegetation management.
Combine internal and external data sources (eg, satellite imagery, drone-data, precipitation/moisture) to prioritize field activities in context of cost and risk.
Reliability management.
Minimize unplanned outages at lower cost of service through predictive maintenance programs.

News & Blog

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8 Security Standards For Safeguarding Customer Data

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