Publication Date
2-1-2025
Document Type
Article
Publication Title
Smart Cities
Volume
8
Issue
1
DOI
10.3390/smartcities8010030
Abstract
Highlights: What are the main findings? A smart green energy management system integrating machine-learning model and reinforcement learning is proposed in this work to optimize energy consumption and solar generation for a campus environment. The integrated model demonstrated superior prediction performance with an RMSE of 14.72 for energy consumption and 75.45 for solar generation. What is the implication of the main finding? An efficient solution for campuses is provided to reduce grid dependency and enhance energy sustainability. A benchmark is established for future green campus initiatives by integrating advanced machine learning and reinforcement learning techniques. The increasing demand for energy efficiency and the integration of renewable energy sources have become crucial for sustainability in modern campuses. This work presents a smart green energy management system (SGEMS) that integrates a machine learning model and reinforcement learning (RL) to optimize energy consumption and solar generation across a green campus. Using historical data from three campus buildings, we developed a predictive model to forecast short-term energy consumption and solar generation. The XGBoost algorithm, combined with RL, demonstrated superior performance in predicting energy consumption and generation, outperforming other models with a root mean square error (RMSE) of (Formula presented.), a mean absolute error (MAE) of (Formula presented.), and a mean absolute percentage error (MAPE) of (Formula presented.). This work proposes a web-based interface for real-time energy monitoring and decision-making, helping users forecast power shortages and manage energy usage effectively. The proposed approach provides a scalable solution for campuses aiming to reduce reliance on external grids and increase energy efficiency, setting a benchmark for future green campus initiatives.
Keywords
energy consumption forecasting, machine learning, reinforcement learning, smart green energy management, solar energy prediction
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Applied Data Science
Recommended Citation
Charan Teja Madabathula, Kunal Agrawal, Vijen Mehta, Swathi Kasarabada, Sai Srimai Kommamuri, Guannan Liu, and Jerry Gao. "Smart Green Energy Management for Campus: An Integrated Machine Learning and Reinforcement Learning Model" Smart Cities (2025). https://doi.org/10.3390/smartcities8010030
