AI Demand Forecasting Engine

Machine learning pipeline for gas demand forecasting combining statistical methods with weather-driven features, achieving 95%+ accuracy.

AIMachine LearningPythonForecastingAnalytics

A predictive analytics platform applying machine learning to operational data from gas distribution networks. The forecasting module uses ensemble methods trained on historical consumption data, weather patterns, and calendar features to predict demand at various time horizons.

Achieved 95%+ accuracy using statistical modeling, scikit-learn, and time series forecasting (ARIMA, Prophet) with NumPy/SciPy/Pandas for scientific computing. The system includes anomaly detection for identifying unusual patterns in metering data that may indicate commercial losses or equipment failures.