Build end-to-end energy ML: feature engineering, Random Forest, drift/SPC, SHAP explainability & operations workflows
This course contains the use of artificial intelligence
Energy efficiency is no longer just about traditional audits. Modern organizations need predictive models to forecast consumption, detect abnormal behavior early, and continuously monitor model health in production.
What you’ll learn
- Apply AI and data analytics to improve energy efficiency and move from traditional audits to intelligent, data-driven optimization..
- Understand and prepare energy datasets for machine learning using real industrial and building energy data workflows..
- Build Python data pipelines for energy monitoring, cleaning, feature engineering, and automated energy performance analysis..
- Develop predictive machine learning models to forecast consumption, detect anomalies, and support energy decision-making..
Course Content
- AI for Energy Efficiency From Traditional Audits to Data-Driven Optimization –> 8 lectures • 29min.
- Understanding Energy Data for Machine Learning –> 10 lectures • 42min.
- Python & Data Engineering for Energy –> 7 lectures • 29min.
- Machine Learning Foundations for Engineers –> 14 lectures • 1hr 13min.
- Predictive Modeling for Energy Efficiency –> 19 lectures • 2hr 5min.
- Advanced Monitoring Techniques –> 1 lecture • 21min.
Requirements
This course contains the use of artificial intelligence
Energy efficiency is no longer just about traditional audits. Modern organizations need predictive models to forecast consumption, detect abnormal behavior early, and continuously monitor model health in production.
In this course, you will build a complete, production-oriented workflow for AI applied to energy efficiency:
- Start with the fundamentals of energy data (load curves, energy performance indicators, weather/production dependency)
- Learn Python and time-series data engineering for real energy datasets
- Build and validate forecasting models (Linear Regression → Random Forest) with correct evaluation and time-aware validation
- Move into advanced monitoring: anomaly detection (residual methods + Isolation Forest), drift detection with rolling KPIs, SPC control charts, and explainable AI with SHAP
- Learn how to operationalize insights: severity levels, routing and escalation, work orders, and feedback loops
Premium learning experience: Each section includes practical assets (slides, Python labs, datasets, handbooks, and quizzes with solutions). You will follow along and also complete hands-on exercises to build a portfolio-ready capstone.By the end, you won’t just “know the theory.” You’ll know how to implement monitoring systems that prevent failures, reduce downtime, and deliver measurable savings.Requirements- Basic Python recommended (variables, functions). You’ll be guided step-by-step for the data parts.
- A laptop/PC with Python (Anaconda recommended)
- No prior machine learning experience required (we cover fundamentals)
Who this course is for
- Energy engineers, facility managers, HVAC/industrial engineers
- Data analysts who want to specialize in energy analytics
- Engineering students (electrical, mechanical, industrial)
- Professionals building forecasting + monitoring pipelines for buildings or industrial processes
“ You get a complete practice pack:- Slides (PDF/PPT)
- Python Lab Notebook (IPYNB)
- Dataset (CSV)
- Deep Handbook (PDF)
- Quiz + Solutions (PDF)
How to use it:
- Watch the lecture first
- Then open the notebook and run it with the dataset
- Finally, take the quiz to confirm mastery”