Feeder-Level Load Forecasting and Peak-Shaving Advisory Platform for Urban Discoms

Objective: To enable urban electricity distribution companies (Discoms) to predict feeder-wise peak loads and provide actionable advisories for demand-side management.

India's urban Discoms face frequent challenges in accurately predicting feeder-level electricity demand and managing peak loads, leading to grid stress, increased operational costs, and consumer outages. This issue is critical for municipal power departments, especially as urbanization and distributed renewable energy adoption accelerate, mapping to SDG 7 (Affordable and Clean Energy).

The proposed solution is a digital analytics platform that integrates historical feeder data, weather information, and socio-economic indicators to generate predictive demand-side load forecasts and automated peak-shaving advisories for feeder operators. The system will recommend actionable interventions such as scheduled demand response events, load shifting, and consumer notifications.

Key deliverables include a working prototype of a web-based dashboard with real-time data integration, ML-driven forecasting models, advisory engines, and visualization tools. The platform will support integration with open Discom datasets and weather APIs, and provide role-based access for Discom engineers and municipal officials.

The solution will help reduce peak load incidents, improve grid reliability, and optimize resource allocation. It is designed for scalability across different urban Discoms and can be extended to rural feeders or integrated with smart grid initiatives for wider social impact.