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In alignment with the global Environmental, Social, and Governance (ESG) trend, building energy conservation has emerged as a critical issue. This study explores the application of an energy management system in Feng Chia University’s in-SenseLab, leveraging the Home Assistant open-source platform, an ESP32 microcontroller, a PZEM-004T v3.0 energy monitoring module, and a DHT11 environmental sensor. The proposed system is characterized by its low cost and ease of deployment, offering real-time energy consumption monitoring, automated control of electrical equipment, and comprehensive data visualization and analysis capabilities. Over a three-month operational period, the in-SenseLab demonstrated substantial improvements in energy efficiency. Additionally, the implemented mechanisms for abnormal electricity consumption detection and equipment performance evaluation have effectively reduced operational costs and minimized carbon emissions. The results validate the feasibility of utilizing the Home Assistant platform for laboratory energy management. Future research will focus on integrating artificial intelligence (AI) for predictive energy optimization and fostering cross-disciplinary collaboration to develop more holistic building energy management solutions. These efforts aim to contribute meaningfully to achieving the United Nations’ Sustainable Development Goals (SDGs).

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Integrating Smart Technologies for an Enhanced Energy Management System at in-Sense Lab of Feng Chia University

  • 会议论文

  • 发表日期：

    2025年

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  • Szu-Wei Fang,
  • Wei Lin,
  • Shwu-Ting Lee

摘要

In alignment with the global Environmental, Social, and Governance (ESG) trend, building energy conservation has emerged as a critical issue. This study explores the application of an energy management system in Feng Chia University’s in-SenseLab, leveraging the Home Assistant open-source platform, an ESP32 microcontroller, a PZEM-004T v3.0 energy monitoring module, and a DHT11 environmental sensor. The proposed system is characterized by its low cost and ease of deployment, offering real-time energy consumption monitoring, automated control of electrical equipment, and comprehensive data visualization and analysis capabilities. Over a three-month operational period, the in-SenseLab demonstrated substantial improvements in energy efficiency. Additionally, the implemented mechanisms for abnormal electricity consumption detection and equipment performance evaluation have effectively reduced operational costs and minimized carbon emissions. The results validate the feasibility of utilizing the Home Assistant platform for laboratory energy management. Future research will focus on integrating artificial intelligence (AI) for predictive energy optimization and fostering cross-disciplinary collaboration to develop more holistic building energy management solutions. These efforts aim to contribute meaningfully to achieving the United Nations’ Sustainable Development Goals (SDGs).