Energy monitoring system for textile industry
Date
2025-05
Journal Title
Journal ISSN
Volume Title
Publisher
BRAC University
Abstract
This project introduced energy monitoring system for textile industry in Bangladesh. It combines IoT hardware with NILM methodology to carry out the real-time monitoring of individual machine level energy consumption. Two methods i.e. (Intrusive Load Monitoring) ILM and (Non Intrusive Load Monitoring) NILM were compared among which NILM was chosen for its cost effectiveness, scalability and ease of adaptability. The network monitors the total voltage and current with sensors and sends the data to the cloud server with ESP8266. A deep learning model MATNilm was developed to disaggregate the total energy into machine wise power consumption with acceptable accuracy (the average F1-score: 0.73). Real time display via Google Sheets with Blynk app is enabled. The product is conducive for remote monitoring, energy audit and adheres to national and international efficiency goals. Upcoming work will help refine model accuracy, on edge deployment and predictive maintenance. The system provides an industrial application for energy optimization.
Description
Cataloged from PDF version of final year design project.
Includes bibliographical references (pages 88-90).
This final year design project is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2025.
Includes bibliographical references (pages 88-90).
This final year design project is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2025.
Keywords
Energy monitoring system, Textile industry, IoT hardware, Non-intrusive load monitoring, Intrusive load monitoring, Deep learning model, Real-time monitoring, F1-score, Predictive maintenance, Energy audit, Remote monitoring