№ 03 — Project
AI Demand Forecasting Engine.
Machine learning pipeline for gas demand forecasting combining statistical methods with weather-driven features, achieving 95%+ accuracy.
№ 03.01
Context
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.
№ 03.02
Approach
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.
№ 03.04
Stack
01
AI
02
Machine Learning
03
Python
04
Forecasting
05
Analytics