📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI For Energy Load Forecasting In Smart Grids, from foundations to a certified capstone project.
Outline
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
Outline
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
Outline
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
Earn government-registered certification in AI For Energy Load Forecasting In Smart Grids
e-Certificate and e-Marksheet issued on successful completion.
