Development of Smart-Meter Load Forecasting and Demand-Response Engine for Indian Utili...

Objective: To design and implement a data-driven engine that forecasts power demand from smart meters and enables demand-response strategies for Indian urban utilities.

In India, urban distribution companies face acute challenges in forecasting electricity demand and managing peak loads due to rapid growth, irregular usage patterns, and limited real-time visibility. Smart meters are being deployed to improve data granularity but extracting actionable insights for demand-response remains underutilized.

The project involves collecting and analyzing smart meter consumption data from a real or simulated dataset (such as the UPPCL smart meter pilot), designing machine learning models for short-term load forecasting, and building a prototype engine to trigger automated demand-response actions (e.g., alerts, load shifting incentives).

Deliverables include a cleaned dataset, forecasting model (with baseline and advanced ML approaches), simulation of demand-response scenarios, dashboards/visualizations, and a technical report with actionable recommendations.

The solution supports Indian utilities in optimizing grid operations, reducing peak loads, and enabling data-driven decision-making for tariff design, infrastructure investments, and consumer engagement.