Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI-Driven Energy Solutions: Forecasting, Optimization, and Renewables brings a solutions focus to energy AI. You learn to build the forecasting that energy systems depend on — demand and renewable generation — and the optimisation that acts on it, from dispatch and storage to efficiency. The course centres on integrating variable renewables reliably and turning predictions into concrete operational solutions. Grounded in real energy data, it connects analytics to measurable outcomes. You finish able to design an AI-driven solution to an energy problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI-driven energy solutions — forecasting, optimisation and renewable integration to make energy systems more efficient, reliable and clean.
📋 Course Objectives
1. Forecast energy demand and renewable output.
2. Optimise dispatch, storage and efficiency.
3. Integrate variable renewables reliably.
4. Turn forecasts into operational solutions.
5. Measure energy and carbon outcomes.
👥 Who Should Enroll?
• Energy engineers and analysts
• Utility and renewables professionals
• Energy data scientists
• Students of energy systems
🚀 Key Learning Outcomes
• The ability to build energy AI solutions.
• A forecasting-and-optimisation project.
• An outcomes-focused energy perspective.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Day 1 – Energy Forecasting with Machine Learning
Apply regression and deep‑learning models to predict solar and wind generation. • Build time‑series models (ARIMA, LSTM) using historic production data. • Deploy forecasting notebooks on Google Colab and export .ipynb deliverables.
Day 2 – Optimization of Smart Grids & Microgrids
Utilize AI algorithms to optimize energy flow and distribution. • Implement mixed‑integer linear programming and reinforcement‑learning optimizers. • Create actionable optimization notebooks for decentralized systems.
Day 3 – Resilience Modeling for Climate‑Adaptive Energy Systems
Model grid resilience against extreme weather and demand spikes. • Integrate scenario‑based AI simulations for adaptive control. • Deliver climate‑resilient system notebooks ready for deployment.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | ARIMA |
| Covered Tool / Platform | LSTM |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Jupyter Notebook |
Frequently Asked Questions
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