Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Energy Load Forecasting in Smart Grids focuses on a foundational problem of the modern grid: predicting how much electricity will be needed, when and where. You learn to build load-forecasting models across short- and long-term horizons using historical demand, weather and calendar data, and to handle the volatility that renewables and electrification introduce. The course connects accurate forecasts to real grid decisions — balancing, dispatch, storage and demand response. You finish able to build and evaluate an energy-load-forecasting model. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to energy load forecasting — predicting electricity demand at multiple horizons to balance and optimise smart grids.
📋 Course Objectives
1. Engineer features from demand, weather and calendar data.
2. Build short- and long-term load forecasts.
3. Handle volatility from renewables.
4. Connect forecasts to grid balancing and dispatch.
5. Evaluate forecast accuracy and uncertainty.
👥 Who Should Enroll?
• Power-systems and energy engineers
• Utility and grid data scientists
• Renewables and demand-response professionals
• Students of energy systems
🚀 Key Learning Outcomes
• The ability to forecast energy load.
• A smart-grid forecasting project.
• A grid-operations 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 – Smart Grid Data Intelligence & Energy Demand Analytics
Explore smart‑grid architecture, AMI, DERs, EVs and micro‑grids • Extract and preprocess smart‑meter, weather and renewable generation data • Identify peak‑demand patterns and seasonal consumption trends
Day 2 – AI‑Based Load Forecasting, Renewable Integration & Explainable Models
Engineer temporal, weather, holiday and renewable features for forecasting • Build and compare ML models (Linear Regression, Random Forest, XGBoost) and DL models (LSTM, GRU, Transformer) • Apply SHAP for explainable AI and evaluate models with MAE, RMSE, MAPE, R²
Day 3 – Demand Response, Grid Optimisation & AI‑Enabled Decision Systems
Design demand‑response strategies: peak shaving, load shifting and dynamic pricing • Simulate grid optimisation using AI forecasts, battery storage and EV charging loads • Create a basic decision‑dashboard with Plotly/Streamlit to visualise insights
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | Plotly |
| Covered Tool / Platform | Streamlit |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | XGBoost |
| Covered Tool / Platform | TensorFlow |
Frequently Asked Questions
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