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Smart Green Energy Management for Campus: An Integrated Machine Learning and Reinforcement Learning Model

Charan Teja Madabathula / Kunal Agrawal / Vijen Mehta / Swathi Kasarabada / Sai Srimai Kommamuri / Guannan Liu / Jerry Gao

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 $14.72$ , a mean absolute error (MAE) of $12.00$ , and a mean absolute percentage error (MAPE) of $2.18 %$ . 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.

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Smart Green Energy Management for Campus: An Integrated Machine Learning and Reinforcement Learning Model

Charan Teja Madabathula / Kunal Agrawal / Vijen Mehta / Swathi Kasarabada / Sai Srimai Kommamuri / Guannan Liu / Jerry Gao

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 $14.72$ , a mean absolute error (MAE) of $12.00$ , and a mean absolute percentage error (MAPE) of $2.18 %$ . 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.

Smart Green Energy Management for Campus: An Integrated Machine Learning and Reinforcement Learning Model

Charan Teja Madabathula / Kunal Agrawal / Vijen Mehta / Swathi Kasarabada / Sai Srimai Kommamuri / Guannan Liu / Jerry Gao

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 $14.72$ , a mean absolute error (MAE) of $12.00$ , and a mean absolute percentage error (MAPE) of $2.18 %$ . 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.

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Titel:

Smart Green Energy Management for Campus: An Integrated Machine Learning and Reinforcement Learning Model

Beteiligte:

Charan Teja Madabathula (Autor:in) / Kunal Agrawal (Autor:in) / Vijen Mehta (Autor:in) / Swathi Kasarabada (Autor:in) / Sai Srimai Kommamuri (Autor:in) / Guannan Liu (Autor:in) / Jerry Gao (Autor:in)

Erschienen in:

Smart Cities, Vol 8, Iss 1, p 30 (2025)

Erscheinungsdatum:

2025

ISSN:

DOI:

https://doi.org/10.3390/smartcities8010030

Medientyp:

Aufsatz (Zeitschrift)

Format:

Elektronische Ressource

Sprache:

Unbekannt

Schlagwörter:

smart green energy management , machine learning , reinforcement learning , energy consumption forecasting , solar energy prediction , Engineering (General). Civil engineering (General) , TA1-2040

Metadata by DOAJ is licensed under CC BY-SA 1.0

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