1Research Scholar, Department of Electrical Engineering, C.V. Raman Global University, Odisha
2Department of Electrical Engineering, C.V. Raman Global University, Odisha
3Department of Electronics and Communication Engineering, C.V. Raman Global University, Odisha
Online published on 29 December, 2025.
India's distribution utilities continue to face high Aggregate Technical and Commercial (AT&C) losses, reliability constraints, and growing integration of variable renewables. The national smart-meter rollout (RDSS) now yields granular, 15–30-minute consumption and event data that enable actionable analytics. This paper presents an India-specific blueprint for applying AI/ML to smart-meter streams across four priority use cases: (i) non-technical loss detection, (ii) shortterm demand forecasting for power purchase planning, (iii) transformer overload/asset-health risk, and (iv) prepaid/ToD customer analytics. We situate these within the Indian metering architecture and synthesize lessons from international deployments. A minimal deployable pipeline and a 12-week pilot design are proposed with measurable targets—AT&C reduction by 1.5–2.5 percentage points in pilot feeders, DT-level forecast MAPE liye ≤7%, improved overload-alarm recall ≥90%, and billing-efficiency gains of 1–1.5 pp. The results offer a pragmatic pathway for DISCOMs to convert smart-meter data into rapid, financially material reliability improvements.
