Scholarlink

This research project presents an approach to energy consumption prediction and management in smart building by integrating an embedded system which includes a robust STM32 microcontroller, ESP32 Wi-Fi module, and XGBoost algorithm as well as a Blynk application. Leveraging historical data and employing the powerful XGBoost algorithm, a bespoke predictive model is developed to capture intricate time-based patterns in energy usage. The STM32 microcontroller in conjunction with sensors and the ESP32 WiFi module, facilitates seamless data collection and transmission to the Blynk application. Rigorous data preprocessing ensures the accuracy of the predictive model. In addition, this study introduces IoT-based energy prediction (IoT-EP) models specifically tailored for smart buildings, addressing the challenges of low forecasting accuracy. Real-world testing on the local electricity grid validates the practicality of the proposed approach.

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STM32–Enhanced IoT Energy Prediction: Leveraging XGBoost for Efficient Management in Smart Buildings

  • 会议论文

  • 发表日期：

    2025年

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  • K. Thirunavukkarasu,
  • Leo Raju,
  • S. Sathishbabu

摘要

This research project presents an approach to energy consumption prediction and management in smart building by integrating an embedded system which includes a robust STM32 microcontroller, ESP32 Wi-Fi module, and XGBoost algorithm as well as a Blynk application. Leveraging historical data and employing the powerful XGBoost algorithm, a bespoke predictive model is developed to capture intricate time-based patterns in energy usage. The STM32 microcontroller in conjunction with sensors and the ESP32 WiFi module, facilitates seamless data collection and transmission to the Blynk application. Rigorous data preprocessing ensures the accuracy of the predictive model. In addition, this study introduces IoT-based energy prediction (IoT-EP) models specifically tailored for smart buildings, addressing the challenges of low forecasting accuracy. Real-world testing on the local electricity grid validates the practicality of the proposed approach.