EV Charging Infrastructure Optimization

A Data-Driven Approach using XGBoost and MCDM

Welcome

This is the comprehensive project report for “Data-Driven Optimization for EV Charging Infrastructure,” submitted in partial fulfillment of the requirements for Sabudh Major Passion-Project.

Use the navigation bar on the left to explore the chapters of this report.

Project Summary

• Goal: To identify optimal locations for EV charging stations using machine learning.
• Method: XGBoost Regression combined with Multi-Criteria Decision Making (MCDM).
• Outcome: A prioritized map of the top 300 locations for immediate infrastructure investment.

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Guo, Su, and Huiru Zhao. 2016. “Optimal Siting and Sizing of Electric Vehicle Charging Stations: A Review.” International Journal of Energy Research 40 (10): 102–20.

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McKenna, Russell, Mogens Graf Plessen, and Frances Sprei. 2020. “High-Resolution Geospatial Assessment of Residential Electric Vehicle Charging Demand.” Nature Energy 5 (10): 101–10.

Morro-Mello, I., and A. Padilha-Feltrin. 2021. “Machine Learning Approaches for Electric Vehicle Charging Infrastructure Planning: A Review.” Journal of Energy Storage 36: 102353.

Name, Author, and Co-Author Name. 2023. “HERO: A Hybrid Energy Resource Optimization Dataset for EV Charging Stations.” Journal of Renewable and Sustainable Energy 10 (4): 100–115.

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Wu, Yuhua, Tao Zhang, and Rui Zhang. 2019. “Site Selection of EV Charging Stations Using Fuzzy MCDM Based on GIS.” Sustainable Cities and Society 50: 101686.