About Workshop
Aim
What Participants Will Learn
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Access and curate real-time IESO/AESO and weather streams; engineer time-series features.
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Build, tune, and evaluate ML baselines (RandomForest, XGBoost, LightGBM) for demand prediction.
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Apply deep learning (LSTM/GRU/Transformers) for multi-step forecasting (1h/24h/7d).
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Diagnose models with MAE/RMSE/MAPE and residual/feature-importance analyses.
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Detect peaks and anomalies to support grid operations and market decisions.
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Compare ML vs DL trade-offs and select production-ready approaches.
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Deliver a working 24-hour forecast model, an interactive “actual vs predicted” dashboard, and an integration plan for API deployment.
Structure
📅 Day 1 – Foundations: Energy Data Access & Feature Engineering
- Introduction to Canadian energy systems: IESO & AESO grid operations and market structures
- Accessing real-time energy APIs: Ontario demand, Alberta markets, and weather data sources
- Hands-on: Fetching & exploring IESO/AESO data using Python
- Feature engineering for time-series: temporal features (hour, day, season), weather integration, lagged demand
- Data preprocessing: handling missing values, normalization, outlier detection
- Hands-on: Building the initial energy dataset with Pandas/NumPy
📅 Day 2 – ML for Demand Prediction
- ML algorithms for energy forecasting: RandomForest, XGBoost, LightGBM
- Feature importance & descriptor engineering (temporal, weather, calendar effects)
- Hands-on: Training baseline models with Scikit-learn
- Model evaluation: MAE, RMSE, MAPE, directional accuracy
- Hands-on: Visualization (actual vs predicted, residuals, feature importance)
- Peak demand prediction & anomaly detection
📅 Day 3 – Deep Learning for Time-Series
- Introduction to deep learning for energy forecasting: LSTM, GRU, Transformers
- Sequence modeling and attention mechanisms
- Hands-on: Building an LSTM model with TensorFlow/PyTorch
- Multi-step forecasting (1-hour, 24-hour, 7-day ahead)
- Hands-on: Comparing classical ML vs deep learning performance
- Real-world deployment considerations & API integratio
🧰 Technical Stack
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- Data: IESO API, Supabase streaming endpoints
- ML: Scikit-learn, XGBoost, LightGBM
- DL: TensorFlow/PyTorch (LSTM, Transformers)
- Visualization: Plotly, Matplotlib, Recharts
Important Dates
Registration Ends
Workshop Dates
What You Will Gain
Outcomes
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A working 24-hour-ahead demand forecasting model built on IESO/AESO + weather data
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An interactive dashboard visualizing actual vs. predicted load, residuals, and peak flags
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Comparative report showing ML vs. DL performance (MAE/RMSE/MAPE, directional accuracy)
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Reproducible data pipeline: API ingestion, preprocessing, feature engineering, and validation
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Skills in training and tuning RF/XGBoost/LightGBM and LSTM/Transformers for multi-step horizons
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Methods for peak-demand prediction, anomaly detection, and uncertainty communication
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A lightweight deployment plan (API endpoints, monitoring/drift checks, retraining cadence)
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All notebooks, code templates, and study materials.
Who Should Attend
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Energy analysts, data scientists, and ML engineers working with time-series data
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Grid/market engineers in utilities, ISO/RTOs, and smart-grid companies
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Energy trading desks, quant researchers, and analytics teams
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Senior undergraduates, postgraduates, and early-career professionals in data/energy domains
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Prerequisites: basic Python and introductory stats/ML (time-series familiarity helpful, not required)