EXPERIMENTAL PERFORMANCE SET UP & MAS-Grid-LAB-D-SIMULATION OBSERVATAION - A Multi-Agent Reinforcement Learning Approach for Smart Grid Optimization and Real-Time Energy Management " PHASE-II

1.🌱 AI-Powered Energy Efficiency and Climate-Responsive Forecasting System

A research framework integrating AI-based forecasting, anomaly detection, and multi-agent optimization to enhance energy efficiency in smart grids, compliant with ISO 50001 and UN SDG 7. - ✈️ Powered by Google Cloud or ✈️ CI/CD Ready ✈️ Fast-track your smart grid simulation *✈️ Launch-ready AI *forecasting modules - Focuses on integrating multiple AI models(LSTM, Prophet, ARIMA) with GridLAB-D simulation, agent coordination, a Streamlit dashboard, optimization scripts, and deployment configurations (Docker, Azure, CI/CD templates). - This repository contains the simulation code, forecasting models (LSTM, Prophet, ARIMA), and optimization workflow for an MAS-based Smart Grid architecture, real-time energy forecasting, climate-resilient DECISION MAKING.

🧠 AI & ML Frameworks

☁️ Cloud & Deployment

📊 Visualization & Dashboard

📈 Forecasting Models in Colab

🌍 Standards & Impact

📦 Repository Insights

2. 📌 Objectives

• Develop a modular Multi-Agent System (MAS) integrating:
  • Short-term forecasting (LSTM, ARIMA, Prophet)
  • Real-time anomaly detection (Autoencoders, Isolation Forest)
  • Grid optimization using mathematical modeling (Pyomo)
• Leverage public and sensor-based datasets from India for real-time modeling
• Deploy on Google Cloud/Azure with open dashboards and APIs
• Align with international energy standards and climate goals

🧠 Methodology

3.1 📊 Data Collection

| Source | Data Type | Description | |--------|-----------|-------------| | CEA (Central Electricity Authority) | Energy Generation, Load Data | National grid-level metrics | | POSOCO | Real-time Grid Frequency, Demand, Load Forecast | Operational grid data | | IMD | Temperature, Rainfall, Humidity, Wind | Weather factors for forecasting | | MNRE | Renewable Energy Reports | Solar/wind capacity, performance | | IoT/SCADA Sensors | Smart Meter + Real-time Usage | Local microgrid data and anomalies |

3.2 🧠 Model Development

🔮 Forecasting Models

Accurately predict short-term energy demand and renewable generation using:

• LSTM (Long Short-Term Memory): Deep learning model for multivariate time-series forecasting
  
• ARIMA (AutoRegressive Integrated Moving Average): Classical statistical model for baseline comparison
  
• Prophet: Robust to seasonality, holidays, and missing data – ideal for real-world deployment

Output: Energy demand (MW), renewable generation (solar/wind), load curves

🚨 Anomaly Detection Models

Early detection of irregular consumption, grid faults, or forecast errors:

• Isolation Forest: Efficient for high-dimensional, sparse data anomalies
  
• Autoencoders (Deep Learning): Learn normal behavior and flag deviations in usage patterns

Use Case: SCADA/sensor stream analysis for operational fault detection or sudden surges/drops

⚙️ Optimization Module

Balance energy supply and demand, minimize cost and loss:

• Pyomo (Python Optimization Modeling Objects):
  • Linear and non-linear optimization
    
  • Formulate grid dispatch, storage scheduling, renewable prioritization
• Constraints: Capacity limits, peak load hours, weather uncertainty
  
• Objectives: Cost minimization, loss reduction, grid stability

🧩 Agent-Based System Architecture

Intelligent agents coordinate and communicate to act autonomously based on roles:

• Platform:

  • JADE (Java Agent Development) for scalable agent systems
    
  • Or Python-based MAS using aiomas / spade for integration ease

• Agent Roles:

  • Forecasting Agent`: Supplies energy/load predictions
    
  • Anomaly Agent`: Flags abnormal patterns
    
  • Optimization Agent`: Recommends optimal dispatch
    
  • Coordinator Agent`: Orchestrates decision-making and API calls

🔁 Model Interaction Diagram (Mermaid)

`` mermaid

graph TD A[Data Inputs: Weather, Load, Gen Data] --> B[Forecasting Agent (LSTM/Prophet)] A --> C[Anomaly Agent (Autoencoder/IF)] B --> D[Optimization Agent (Pyomo)] C --> D D --> E[Coordinator Agent] E --> F[Streamlit Dashboard/API] ``````

📦 ai-smart-grid-mas/ WORKFLOW

``` ├── README.md <-- Project overview, abstract, architecture

├── /notebooks <-- Jupyter notebooks for LSTM, Prophet, ARIMA

├── /src <-- Python modules (agent logic, optimization)

├── /data <-- Sample dataset (cleaned and anonymized)

├── /models <-- Trained models (optional)

├── requirements.txt <-- Python dependencies

├── LICENSE <-- MIT or Apache License

├── /figures <-- Plots, model diagrams

├── /simulation <-- GridLAB-D config, RL agents ```

📁 Repository Structure: ai-smart-grid-mas/

├── README.md <- Project overview, setup, usage, citations ├── LICENSE <- MIT License ├── requirements.txt <- pip dependencies ├── app.py <- Streamlit app for live forecast + anomaly detection │ ├── /data/ │ └── sample_energy_data.csv <- Example multivariate dataset (solar, wind, load) │ ├── /notebooks/ │ ├── forecasting_lstm.ipynb <- LSTM with Keras for energy forecasting │ ├── prophet_model.ipynb <- Prophet with changepoint detection │ └── arima_model.ipynb <- ARIMA for baseline forecasting │ ├── /src/ │ ├── agent.py <- Core forecasting and anomaly detection agent │ ├── optimization.py <- Pyomo-based optimization logic │ ├── rl_agent.py <- (Optional) RL for smart decisions │ └── communication.py <- Handles inter-agent scheduling & messaging │ ├── /simulation/ │ └── gridlabd_config.glm <- GridLAB-D sample model (can be extended) │ ├── /models/ │ └── pretrained_model.pkl <- Serialized forecasting model (e.g., LSTM) or demo model │ ├── /figures/ │ └── mas_architecture.png <- Architecture, flowcharts, plots │ └── /docs/ └── paper_summary.pdf <- Summary for publication or Zenodo DOI

3.3 🧰 Tools & Platforms

| Category | Tools / Platforms | Use | |---------|-------------------|-----| | Programming & ML | Python, Jupyter, Pyomo | Model training, simulations | | AI Frameworks | TensorFlow, Keras, Prophet | Forecasting and anomaly models | | Visualization | Streamlit, Power BI, Seaborn | Dashboards & data exploration | | Cloud & Hosting | Google Cloud / Azure | Deployment, API management | | Web Interface | FastAPI, Flask | REST APIs and front-end interface | | Version Control | GitHub | Collaboration and versioning | | Open Repository | Zenodo | Dataset/code archiving with DOI |

4. ✅ Final GitHub-Ready(Package)

ai-smartgrid-mas/ ├── .github/workflows/ │ └── deploy.yml ← GitHub Actions for auto-deploy ├── app/ │ └── app.py ← Clean Streamlit dashboard ├── data/ │ └── [all your CSVs] ├── docs/ │ ├── index.md ← mkdocs homepage │ ├── architecture.md ← MAS Architecture │ └── simulation.md ← Case study & Results ├── figures/ │ └── [diagrams, logos, etc.] ├── notebooks/ │ ├── forecasting_lstm.ipynb │ ├── prophet_forecasting.ipynb │ └── [your Jupyter notebooks] ├── src/ │ ├── agent.py ← Forecasting, Anomaly agents │ ├── optimization.py ← Pyomo/Dispatch logic │ ├── communication.py ← Socket/pub-sub logic │ └── rl_agent.py ← DQN/Policy Gradient agent ├── styles/ │ └── [optional custom CSS] ├── zipped/ │ └── [your uploaded zip files] ├── .gitignore ├── .nojekyll ├── CITATION.cff ├── LICENSE ├── mkdocs.yml ├── README.md ├── requirements.txt └── zenodo.json `

🚀 How to Run(GitHub)

```bash pip install -r requirements.txt python src/agent.py

python run_simulation.py

```

Run the Application

Backend: Flask/FastAPI Dashboard: Streamlit/Power BI Cloud: Azure / GCP

bash python main.py Or launch the interactive dashboard: bash streamlit run app/dashboard.py

5. ☁️Deploy to Cloud (Google Cloud Run / Azure App Service)

1. Create a project on Google Cloud / Azure
2. Enable Cloud Run or App Service
3. Use the Dockerfile for containerized deployment:

bash docker build -t ai-energy-mas . docker run -p 8501:8501 ai-energy-mas` ``

📜Citation(IEEE)

If you use this code in your research, please cite the paper:

Bahamnia, I. (2025). "Hybrid Forecasting and MAS Optimization for Smart Grids", Elsevier Energy and AI.

🔗 Useful Links

• UGC - MTech thesis and dissertation ( publication )
• 🌐 Zenodo Project Archive
• 📊 Live Dashboard (Demo)
• 📖 Publication Target – IEEE Access
• 🔄 Version Control – GitHub