Exicom EVPredAI - Advanced EV Charging Station Predictor

This application leverages cutting-edge machine learning algorithms and geospatial analysis to help users find optimal EV charging stations and provides intelligent recommendations tailored to Exicom's charging infrastructure.

Project Overview

EVPredAI is a sophisticated decision support system that combines real-time geospatial data, machine learning, and user behavior analysis to optimize electric vehicle charging experiences. The system employs a multi-modal approach to predict optimal charging station locations and provide intelligent recommendations to EV users.

Key Features

• Intelligent Charging Station Location Prediction: Uses advanced ML algorithms to predict optimal placement of new charging stations
• User-Centric Recommendation Engine: Provides personalized charging recommendations based on user behavior patterns
• Geospatial Analysis: Integrates OSM data with proprietary location intelligence algorithms
• Interactive Map Visualization: Dynamic visualization of charging stations with demand heatmaps
• Natural Language Interface: AI-powered chatbot for intuitive user interactions

Technical Architecture

EV Charging Location Prediction Algorithm

Our location prediction algorithm employs a sophisticated ensemble approach that combines:

1. Gradient Boosted Decision Trees (GBDT): Core predictive engine that analyzes over 20 geospatial and temporal features to identify optimal charging locations

   • Uses XGBoost with hyperparameter optimization via Bayesian search
   • Features engineering includes population density, traffic patterns, points of interest proximity, and grid capacity metrics

2. Spatial Autocorrelation Analysis: Implements Moran's I and LISA statistics to identify spatial clusters and patterns in charging demand

3. Time Series Forecasting: Incorporates SARIMA models to account for temporal variations in charging demand

4. Multi-criteria Decision Analysis: Weighted overlay of multiple factors for final location scoring:

   Location Score = β₁(Population Density) + β₂(POI Proximity) + β₃(Traffic Flow) + β₄(Grid Capacity) + ... + βₙ(Factor_n)
   

   Where β represents optimized weights determined during model training.

Fine-tuned NLP Model for Exicom-Specific Queries

The system includes a specialized NLP model fine-tuned specifically for Exicom's EV charging domain:

• Base Architecture: Built on transformer-based architecture adapted from BERT/RoBERTa
• Domain Adaptation: Fine-tuned on 15,000+ EV charging specific conversations and technical documentation
• Custom Entity Recognition: Trained to recognize Exicom-specific charging equipment, protocols, and terminology
• Intent Classification: Optimized for understanding complex user queries related to charging station availability, compatibility, and technical specifications
• Context-Aware Responses: Maintains conversation state to provide coherent multi-turn interactions

Model Files (Not Included in Repository)

The application uses several machine learning models that are not included in the repository due to their large size (>100MB per file) and GitHub's file size limitations.

Required Model Files

The following model files must be downloaded separately:

• ev_charging_model.joblib - Primary location prediction model (678KB)
• ev_charging_scaler.joblib - Feature normalization model (1.3KB)
• trained_features.joblib - Feature definition file (133B)
• tapas_base_model.h5 - Fine-tuned NLP model for Exicom-specific queries (422MB)

How to Download Pre-trained Models

The pre-trained models can be accessed through Exicom's secure model registry:

1. Request access to the Exicom ML Model Registry
2. Download the models using the provided credentials:

   # Example download script (requires authentication)
   python scripts/download_models.py --api-key YOUR_API_KEY

Training Your Own Models

Location Prediction Model Training

To train your own location prediction model:

1. Prepare your training data in the required format:

   location_id, latitude, longitude, population_density, poi_count, traffic_flow, ...
   

2. Run the training pipeline:

   python predictor.py --train --data_path your_data.csv --epochs 100 --learning_rate 0.01

3. Evaluate the model performance:

   python predictor.py --evaluate --model_path your_model.joblib

The training process employs a 5-fold cross-validation strategy and automatically tunes hyperparameters using Bayesian optimization. The process typically requires 4-6 hours on a system with 16GB RAM and modern CPU.

NLP Model Fine-tuning

The NLP model was fine-tuned using a proprietary dataset of EV charging conversations and Exicom technical documentation:

1. We started with a pre-trained language model
2. Applied domain adaptation using 15,000+ charging-related conversations
3. Further fine-tuned on 5,000+ Exicom-specific technical interactions
4. Optimized using LoRA (Low-Rank Adaptation) to reduce computational requirements while maintaining performance

To fine-tune your own model, you would need:

• A corpus of domain-specific text (min. 10,000 examples)
• Compute resources: 1 GPU with 16GB+ VRAM or cloud compute equivalent
• 12-24 hours of training time

Setup Instructions

1. Clone this repository
2. Install dependencies:

   npm install
   python -m pip install -r requirements.txt

3. Create a .env file with necessary environment variables (see template in .env.template)
4. Download required model files as described above
5. Run the application:

   npm run dev
   python app.py

Development and Deployment

Development Environment

For local development:

1. Set up a virtual environment: python -m venv .venv
2. Activate it: source .venv/bin/activate (Unix) or .venv\Scripts\activate (Windows)
3. Install dependencies: pip install -r requirements.txt
4. Install Node.js dependencies: npm install
5. Run both frontend and backend servers

Production Deployment

For production:

1. Build the frontend: npm run build
2. Deploy backend using gunicorn/uvicorn
3. Configure environment variables for production settings
4. Set up proper SSL certificates for secure communication

Technologies Used

• Frontend: React 18, TailwindCSS, Shadcn UI components, Vite
• Backend: Python 3.12, Flask, FastAPI
• Machine Learning: Scikit-learn, XGBoost, PyTorch, Transformers
• Data Processing: Pandas, NumPy, Parquet
• Geospatial Analysis: Folium, OSMnx, GeoPandas
• NLP: LangChain, Sentence-Transformers, Faiss for vector search
• Visualization: D3.js, Plotly

Contributing

Please see CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

License

This project is proprietary and confidential. Unauthorized copying or distribution is prohibited.