AI-Powered Energy Demand Forecasting & Pattern Recognition

Access and curate real-time IESO/AESO and weather streams; engineer time-series features.

Build, tune, and evaluate ML baselines (RandomForest, XGBoost, LightGBM) for demand prediction.

Apply deep learning (LSTM/GRU/Transformers) for multi-step forecasting (1h/24h/7d).

Diagnose models with MAE/RMSE/MAPE and residual/feature-importance analyses.

Detect peaks and anomalies to support grid operations and market decisions.

Compare ML vs DL trade-offs and select production-ready approaches.

Deliver a working 24-hour forecast model, an interactive “actual vs predicted” dashboard, and an integration plan for API deployment.

A working 24-hour-ahead demand forecasting model built on IESO/AESO + weather data

An interactive dashboard visualizing actual vs. predicted load, residuals, and peak flags

Comparative report showing ML vs. DL performance (MAE/RMSE/MAPE, directional accuracy)

Reproducible data pipeline: API ingestion, preprocessing, feature engineering, and validation

Skills in training and tuning RF/XGBoost/LightGBM and LSTM/Transformers for multi-step horizons

Methods for peak-demand prediction, anomaly detection, and uncertainty communication

A lightweight deployment plan (API endpoints, monitoring/drift checks, retraining cadence)

All notebooks, code templates, and study materials.

Energy analysts, data scientists, and ML engineers working with time-series data

Grid/market engineers in utilities, ISO/RTOs, and smart-grid companies

Energy trading desks, quant researchers, and analytics teams

Senior undergraduates, postgraduates, and early-career professionals in data/energy domains

Prerequisites: basic Python and introductory stats/ML (time-series familiarity helpful, not required)