Smart Grid Optimization and Energy Forecasting

UtilityGrid struggled with volatile demand forecasting, leading to high operational costs from inefficient resource allocation and occasional brownouts during peak usage. Their existing model was static and couldn't incorporate real-time weather or industrial output data.

We created a complex data pipeline that ingests grid metrics, weather data, and consumer usage patterns. A custom **predictive model** uses this data to generate highly accurate 24-hour and 7-day demand forecasts, enabling dynamic load shifting and resource pre-allocation.