Energy Loss Heatmap
Generation
Use Case
AI Heatmaps to Detect Hidden Losses and Uncover Theft at Feeder Level
Business Challenge
Distribution companies often struggle to localize energy losses at feeder and transformer levels, making it difficult to distinguish between technical inefficiencies and theft-driven commercial losses.
- High AT&C losses lacked feeder-level visibility, preventing precise localization of loss-prone zones and targeted interventions.
- Absence of real-time monitoring delayed theft detection and loss reporting, reducing the effectiveness of audit programs.
- Fragmented energy audit processes relied heavily on manual inputs, creating scope for delays and errors.
- Lack of granular insights hindered utility planners from justifying CAPEX investments under regulatory schemes.
The AI Approach
To bridge these operational gaps, an AI-powered analytics pipeline was deployed, integrating load flow modeling, energy audits, and automation to generate actionable heatmaps.
- AI-based load flow modeling compared expected versus actual feeder consumption to highlight anomalies at granular levels.
- Sankey diagram-based logic was applied to identify mismatch zones with high probability of technical or commercial losses.
- Automated RPA workflows mapped field inspection data with billing and audit records for loss validation.
- Risk scoring engines ranked feeders and transformers to prioritize theft-prone areas for corrective action.
Project Deployment Overview
Input Data Used
AMR meter data, DT-wise load profiles, and billing records were ingested to enable feeder-level analysis.
Final Output Generated
Feeder and transformer loss heatmaps with risk scores were delivered, highlighting hotspots for corrective actions.
Deployment Platform
GridSense™ Loss Map Engine was deployed to analyze large-scale distribution datasets in near real-time.
Processing Scope
Loss mapping workflows were scaled across feeders, transformers, and substations under regulatory compliance frameworks.
Business Outcomes & Value Unlocked
The AI-driven heatmap workflow provided unprecedented feeder-level visibility into AT&C losses, enabling utilities to reduce theft, enhance recovery, justify regulatory-backed investments, improve field inspections, optimize audit programs, and strengthen long-term grid sustainability.
Theft Detection Improved
Uncovered electricity theft across 22 rural feeders, significantly strengthening revenue protection efforts.
Reduced AT&C Losses
Lowered overall AT&C losses by 7.3% within the first year of deployment, boosting grid efficiency and reducing financial leakage significantly.
Regulatory Compliance Enabled
Provided data-backed justification for AI-driven energy audit programs under the RDSS framework.
Risk-Based Prioritization
Ranked feeders with commercial and technical risk for targeted investments and preventive actions.