Real-World Use Cases

See how eSUMS delivers measurable business value across revenue optimization, risk quantification, and uptime maximization.

Sibaya Casino

Limited Historical Data: Achieving Forecasting Accuracy in 6 Months

Industry Commercial
Capacity 1.5 MW
Location Durban, South Africa

Challenge

Only 6 months of historical data available for a 1.5MW solar plant. Industry standard requires 24+ months for accurate forecasting models, creating a 27-40 month deployment timeline before reliable predictions are possible.

7%
SMAPE Accuracy
6
Months to Accuracy

Results

  • Achieved 7% SMAPE accuracy in 6 months using transfer learning
  • Leveraged neural networks trained on similar Durban and Johannesburg sites (12-24 months data)
  • Global LSTM modeling approach used external sites as training set
  • Forecasting accuracy from day one vs. industry standard 27-40 month deployment

Cummins Portfolio

Overcoming 65% Data Loss: Maintaining Operations Through Infrastructure Failure

Industry Industrial
Portfolio Size Multi-MW
Data Loss 65% Missing

Challenge

Severe data loss—65% missing data points across multi-MW portfolio. Sensor and telemetry failures made traditional monitoring impossible. Most systems treat missing data as downtime for analytics.

100%
Data Reconstruction
96%
Faster Detection

Results

  • 100% data reconstruction using ARIMA-based interpolation + ML ensemble
  • Zero grid violations maintained despite infrastructure failure
  • 96% faster fault detection despite 65% data loss
  • Maintenance logic continued without interruption—system treated missing data as signal, not downtime

Avaron Infrastructure

Multi-Stakeholder Coordination: Unified Intelligence Across Owner, O&M, and Insurer

Industry IPP/Asset Management
Stakeholders Owner, O&M, Insurer
Sites Multiple

Challenge

Coordinating Owner, O&M contractor, and Insurer across multiple sites. Fragmented systems required manual reconciliation. Each stakeholder needed different intelligence from the same operational data.

70%
Penalty Reduction
100%
Data Reconciliation

Results

  • Unified data layer eliminated reconciliation between stakeholders
  • Economic optimization now considers insurance impact of maintenance timing
  • Multi-stakeholder coordination without data conflicts
  • 70% reduction in unplanned grid code penalties through risk analytics

Edge Deployment Architecture

Real-Time Intelligence: 2-5 Second Forecasts on ARM64 Edge Devices

Platform ARM64 Edge
Devices Raspberry Pi CM4, Orange Pi 5, Rock 5B
Power 3-5W Active

Challenge

Cloud lag costs money in trading. Need real-time decisions without cloud dependency. Connectivity loss shouldn't stop operations. Traditional cloud-based systems introduce latency and single points of failure.

2-5s
Forecast Generation
3-5W
Active Power

Results

  • 2-5 second forecast generation on ARM64 edge devices
  • Operates independently during connectivity loss—forecasting continues at edge
  • 3-5 watts active, 1 watt idle power consumption
  • Edge-native computing enables energy trading, VPPs, and distributed infrastructure optimization

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