Ai For Energy Load Forecasting In Smart Grids

The aim of this workshop is to equip participants with practical knowledge and hands-on exposure to AI-driven smart grid analytics, energy demand forecasting, renewable energy integration, and demand response optimization. The workshop focuses on helping participants understand how energy data can be collected, processed, analyzed, modeled, and transformed into actionable insights for improving grid efficiency, reliability, sustainability, and operational decision-making.

• Understand smart grid architecture, energy data sources, and AI use cases.
• Analyze smart meter data to identify demand patterns and peak-load periods.
• Build AI-based load forecasting models using weather, temporal, and consumption features.
• Evaluate and explain forecasting models using standard metrics and SHAP.
• Explore demand response, grid optimization, EV charging, storage, and renewable balancing.
• Visualize energy insights through charts, simulations, and basic decision dashboards.

📅 Day 1: Smart Grid Data Intelligence & Energy Demand Analytics

• 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

Tools Covered

Google Colab | Python | Pandas | NumPy | Matplotlib | Plotly | Open-Meteo API | Open Power System Data

Hands-on Activity

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.

📅 Day 2: AI-Based Load Forecasting, Renewable Integration & Explainable Models

• 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

Tools Covered

Google Colab | Scikit-learn | XGBoost | TensorFlow/Keras | Statsmodels | Prophet | SHAP | Plotly

Hands-on Activity

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.

📅 Day 3: Demand Response, Grid Optimization & AI-Enabled Decision Systems

• 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

Tools Covered

Google Colab | Python | Plotly | Streamlit | PyPSA | GridLAB-D | OpenDSS

Hands-on Activity

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.

• Researchers and PhD scholars interested in energy systems and AI applications.
• Academicians and faculty teaching or researching smart grids, energy forecasting, or renewable energy.
• Industry professionals working in power utilities, energy management, or smart grid operations.
• Data scientists and ML engineers looking to apply AI for energy load prediction.
• Professionals involved in sustainable energy solutions, demand response, and energy optimization.
• Anyone keen on hands-on experience with AI models for real-world energy forecasting.