Dear Researchers,

We're excited to share our newly published research article titled:
“Smart Green Energy Management for Campus: An Integrated Machine Learning and Reinforcement Learning Model”
in Smart Cities (MDPI), Volume 8, Issue 1, 2025.

Access the full article here:
https://doi.org/10.3390/smartcities8010030

This work presents a smart green energy management system (SGEMS) that integrates machine learning (XGBoost) with reinforcement learning to optimize both energy consumption and solar generation across a university campus setting. Using real-world data from campus buildings, the system achieves high prediction accuracy (RMSE: 14.72 for energy consumption; 75.45 for solar generation) and enables data-driven energy decision-making through a web-based interface.

Key Contributions:

• Integrated ML + RL framework for energy forecasting and optimization.

• Real-time dashboard to support energy-aware decisions for grid independence.

• Campus-wide application using data from the Valahia University of Targoviste (UVTgv), Romania.

• Benchmarks set for future green campus initiatives.

This research offers valuable insights for professionals and researchers working in:

• Smart Cities and Sustainable Campuses

• Renewable Energy Management

• AI Applications in Infrastructure

• IoT-based Energy Systems

I hope you find the article informative and helpful in your work. Please feel free to share it within your networks or reach out if you'd like to discuss it further.

Warm regards,
Charan Teja Madabathula, Kunal Agrawal, Vijen Mehta, Swathi Kasarabada, Sai Srimai Kommamuri, Guannan Liu, and Jerry Gao