Energy Field Services Company
SCADA-to-OAC analytics and Flutter field app for distributed energy assets.
All case studiesAn energy services company operating 45 distributed solar and wind installations across Rajasthan and Gujarat engaged Cydez Technologies to build a centralised analytics and field operations platform. The portfolio comprised 30 solar installations (total 180 MW) and 15 wind installations (total 120 MW), generating combined annual revenue of INR 280 crore from power purchase agreements.
Each site reported performance data via email or phone calls to the central operations team — there was no centralised monitoring capability. Fault detection depended on site caretakers noticing anomalies and calling the engineering team, resulting in an average 48-hour delay between fault occurrence and detection. Energy yield forecasting used manufacturer-provided estimates rather than actual site performance data, leading to 28% forecast error that complicated revenue projections and PPA compliance reporting.
Cydez built a centralised platform connecting SCADA systems from all 45 sites to Oracle Analytics Cloud via OCI. Real-time dashboards displayed energy yield vs. PPA targets, inverter-level efficiency, weather-normalised performance ratios, and grid export data. A Flutter-based field technician app enabled maintenance activity logging, fault photo capture, and AI-prioritised work orders based on asset health scores and revenue impact. ML models trained on actual site performance data and weather forecasts improved energy yield prediction accuracy from 72% to 94%.
- Centralised monitoring for 45 renewable energy sites
- SCADA to OAC analytics pipeline on OCI
- Flutter field technician mobile app
- ML-based energy yield forecasting
- Asset health scoring and prioritised work orders
- PPA compliance reporting automation
An energy services company operating 45 distributed solar and wind installations across Rajasthan and Gujarat had no centralised monitoring capability. Each site reported performance data via email or phone, and field technicians used paper forms for maintenance logs. Energy yield forecasting was based on manufacturer estimates rather than actual site performance data, leading to inaccurate revenue projections and delayed fault detection.
Cydez built a centralised analytics platform connecting SCADA systems from all 45 sites to Oracle Analytics Cloud via OCI. Real-time dashboards displayed energy yield, inverter efficiency, weather-normalised performance ratios, and grid export data. A Flutter-based field app enabled technicians to log maintenance activities, capture fault photos, and receive AI-prioritised work orders based on asset health scores. ML models improved energy yield forecasting accuracy based on actual historical performance and weather data.
How we delivered this project
Discovery
Visited 12 representative sites across Rajasthan and Gujarat over 6 weeks. Documented SCADA system types and communication protocols at each site. Analysed 3 years of energy generation data and maintenance records. Mapped PPA compliance reporting requirements for each off-taker.
Design
Designed the OCI data platform architecture with MQTT-based SCADA data ingestion, OAC dashboards, and ML model serving. Created the Flutter field app UX for use in harsh outdoor environments with glare-resistant design. Designed the energy yield forecasting model using weather data, panel degradation curves, and site-specific performance history.
Development
Built SCADA data ingestion pipelines for 45 sites using MQTT and REST adapters. Deployed OAC dashboards covering portfolio, site, and inverter-level views. Developed the Flutter field app with offline capability for remote sites. Trained ML forecasting models on OCI Data Science using 3 years of generation and weather data.
Launch
Connected sites in 3 waves over 10 weeks. Each wave required SCADA connectivity testing and dashboard calibration. Trained 30 field technicians on the mobile app. Deployed ML forecasting models and validated accuracy against 6 months of actuals before replacing manufacturer estimates in financial projections.
What we built
Portfolio Monitoring
Real-time dashboard showing generation vs. PPA target across all 45 sites. Weather-normalised performance ratio tracking. Automated alerts for underperforming sites relative to peers.
Inverter-Level Analytics
Individual inverter efficiency tracking with degradation trend analysis. Automated detection of string-level faults, MPPT mismatch, and inverter clipping. Root cause classification for performance losses.
Field Technician App
Flutter app for maintenance logging, fault photo capture, spare parts requests, and AI-prioritised work orders. Offline capable for remote sites with satellite backhaul. Glare-resistant UI for outdoor use.
Energy Yield Forecasting
ML models predicting daily and monthly energy yield with 94% accuracy using weather forecasts, panel degradation curves, and site-specific performance history. Revenue impact projections for planning.
Asset Health Scoring
Health scores for every inverter and turbine based on sensor data, maintenance history, and age. Revenue-weighted prioritisation ensuring highest-impact faults are addressed first.
PPA Compliance Reporting
Automated generation of PPA compliance reports for each off-taker. Grid availability tracking, deemed generation calculations, and curtailment logging for contractual claims.
Measurable outcomes
- Fault detection time reduced from 48 hours to 15 minutes
- Energy yield forecasting accuracy improved from 72% to 94%
- Maintenance response time reduced by 55% via mobile-prioritised work orders
- Portfolio-wide performance visibility enabled 8% improvement in energy yield
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