Energy-efficient HVAC System with Decision Tree Classifier and Real-time SMS Notification

Year : 2024 | Volume : 02 | Issue : 02 | Page : 29 34
    By

    Dhanush M,

  • Shivashankar Hiremath,

  • Subramanya R. Prabhu B,

  1. Student, Department of Mechatronics, Manipal Institute of Technology, Manipal Academy Higher Education (MAHE), Manipal, Karnataka, India
  2. Professor, Department of Mechatronics, Manipal Institute of Technology, Manipal Academy Higher Education (MAHE), Manipal, Karnataka, India
  3. Professor, Department of Mechatronics, Manipal Institute of Technology, Manipal Academy Higher Education (MAHE), Manipal, Karnataka, India

Abstract

This research paper explores the design and implementation of an energy-efficient heating, ventilation, and air conditioning (HVAC) system aimed at optimizing energy consumption and enhancing operational efficiency. The system incorporates high-efficiency components, including axial flow fans, motors, and intelligent variable frequency drives, achieving an overall system efficiency of up to 85%. By utilizing both static and dynamic pressures, the HVAC system operates more effectively under varying conditions compared to traditional systems, which primarily rely on static pressure measurements. To further improve system responsiveness, a decision tree classifier is employed to predict the appropriate operational actions based on real-time temperature and slope data. This machine learning approach allows for proactive adjustments to the system’s operation, enhancing its adaptability to changing environmental conditions. The classifier’s performance is evaluated using accuracy, precision, and recall metrics, with results indicating a balanced performance, although with identified areas for improvement. In addition, a real-time SMS notification system is integrated using Twilio, which alerts facility management when the temperature exceeds a threshold of 28 degrees Celsius. This feature guarantees immediate notification about possible problems, enabling rapid intervention. The findings of this study underscore the significant potential of combining energy-efficient technologies with advanced machine learning algorithms in HVAC systems. The findings show that this sort of linkages can result in significant energy savings, enhanced efficiency in operation, and improved safety protocols. Future work will focus on expanding the dataset for the decision tree model and refining its parameters to enhance predictive accuracy, paving the way for more effective energy management solutions in HVAC applications

Keywords: Decision tree classifier, Energy efficiency, HVAC, Smart building technologies, SMS notifications

[This article belongs to International Journal of Advanced Robotics and Automation Technology ]

How to cite this article:
Dhanush M, Shivashankar Hiremath, Subramanya R. Prabhu B. Energy-efficient HVAC System with Decision Tree Classifier and Real-time SMS Notification. International Journal of Advanced Robotics and Automation Technology. 2024; 02(02):29-34.
How to cite this URL:
Dhanush M, Shivashankar Hiremath, Subramanya R. Prabhu B. Energy-efficient HVAC System with Decision Tree Classifier and Real-time SMS Notification. International Journal of Advanced Robotics and Automation Technology. 2024; 02(02):29-34. Available from: https://journals.stmjournals.com/ijarat/article=2024/view=191724


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Regular Issue Subscription Original Research
Volume 02
Issue 02
Received 28/10/2024
Accepted 14/11/2024
Published 19/11/2024
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