Structure

Smart grid architecture: AMI, smart meters, DERs, EVs, storage, microgrids, and grid-edge devices
Global trends in AI-enabled electricity systems and digital energy transition
Energy data sources: smart meter data, weather data, renewable generation data, and grid operation data
Load profiling, peak demand analysis, seasonality, and consumption pattern discovery
Data preprocessing for missing values, outliers, temporal aggregation, and feature extraction
AI use cases in smart grids: forecasting, demand response, fault detection, energy optimization, and grid resilience

Smart Meter Data Analytics & Peak Demand Pattern Discovery

Participants will load and analyze sample electricity consumption data.
They will integrate weather-based features and visualize demand patterns.
They will detect peak-load periods and prepare the dataset for AI-based forecasting.

Short-term, day-ahead, and seasonal load forecasting
Feature engineering using temporal, weather, consumption, holiday, and renewable variables
Machine learning models: Linear Regression, Random Forest, XGBoost, and Gradient Boosting
Deep learning models: LSTM, GRU, and Transformer-based forecasting concepts
Renewable energy integration: solar and wind variability, intermittency, and grid balancing challenges
Model evaluation: MAE, RMSE, MAPE, R², forecast error interpretation, and operational impact
Explainable AI for energy forecasting using feature importance and SHAP

AI Load Forecasting Model with Feature Engineering and Explainability

Participants will build a short-term load forecasting model using smart meter and weather features.
They will compare model performance and visualize forecast accuracy.
They will interpret key demand drivers using explainable AI techniques.

Demand response strategies: peak shaving, load shifting, dynamic pricing, and consumer flexibility
AI for smart grid optimization: cost reduction, energy efficiency, and operational planning
Battery storage and renewable balancing for grid stability
EV charging demand and grid flexibility management
Predictive maintenance, fault detection, and grid resilience applications
Digital twin concepts for future smart grid simulation
Designing AI-enabled decision dashboards for utilities, industries, and smart cities

Demand Response & Grid Optimization Simulation

Participants will use AI-based forecast outputs to simulate a simple demand response scenario.
They will identify peak-load reduction opportunities and estimate cost or energy-saving impact.
They will visualize decision insights through charts or a basic dashboard concept.

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Explain key smart grid components and AI applications.
Preprocess and visualize energy consumption and weather data.
Identify peak demand, seasonality, and load patterns.
Develop and evaluate basic load forecasting models.
Interpret model results using explainable AI techniques.
Simulate demand response and grid optimization scenarios.
Apply AI-based insights to utilities, industries, smart cities, and sustainable energy systems.

Ai For Energy Load Forecasting In Smart Grids

About Workshop