A Load Classification Strategy using NILM and DNN for Potential Demand-Side Management

Arsh Alam Bhatti; Fazeela Irshad Irshad; Abdulelah  Al-Suhaibi; Kamal Shahid; Akbar Ali Khan

Arsh Alam Bhatti Institute of Electrical, Electronics and Computer Engineering, University of the Punjab Lahore, Pakistan
Fazeela Irshad Irshad Inistitue of Electrical Electronics and Computer Engineering , University of the Punjab
Abdulelah Al-Suhaibi Inistitue of Electrical Electronics and Computer Engineering , University of the Punjab
Kamal Shahid Inistitue of Electrical Electronics and Computer Engineering , University of the Punjab
Akbar Ali Khan

Keywords: NILM, Demand Side Management, Deep Neural Networks, Smart Energy Meters, Real Time Energy Monitoring

Abstract

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Uncoordinated and unplanned increase in electricity demand is a critical concern in recent times owing to increasing population and appliance utilization. Significant focus has been given on optimizing load patterns for appliances and capitalizing the potential for savings in domestic energy management. This paper develops a low-cost demand-side management system for residential application through smart energy meters, combined with non-Intrusive load monitoring (NILM) and machine learning for accurate load disaggregation. This paper presents a real-time consumption-based dynamic pricing algorithm, exploiting the use of deep neural networks (DNN) for the classification of essential and non-essential loads with the help of real-time collected datasets. The system provides live energy monitoring through Modbus RTU, RS485 protocols, and a Postgre SQL database, which provides data visualization on a Power BI dashboard highlighting real-time advice on optimization of the consumed energy. The proposed approach demonstrates an effective demand response (DR) mechanism, shifting electricity consumption to off-peak hours throughout out the day without reducing overall energy use, hence optimizing the overall load curve metrics and enhancing energy efficiency.

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Author Biographies

Arsh Alam Bhatti, Institute of Electrical, Electronics and Computer Engineering, University of the Punjab Lahore, Pakistan

Electrical Engineer

Abdulelah Al-Suhaibi, Inistitue of Electrical Electronics and Computer Engineering , University of the Punjab

Electrical Engineer

Kamal Shahid, Inistitue of Electrical Electronics and Computer Engineering , University of the Punjab

Engr. Dr. Kamal Shahid is an Assistant Professor at the Institute of Electrical, Electronics, and Computer Engineering (IEECE), University of the Punjab, Lahore, Pakistan. With an M.Sc. in Computer Engineering and a Ph.D. fellowship from Aalborg University, Denmark, he specializes in communication networks for smart grids. His research focuses on renewable energy integration, IEC-61850-based communication standards, and dynamic data access in power grids. Dr. Shahid plays key roles in academia as Program Coordinator for M.Sc. Electrical Engineering, DDPC member, and IEEE spokesperson. He collaborates globally on innovative solutions for smart grid challenges.

Akbar Ali Khan

Dr. Akbar Ali Khan is a dedicated academic and researcher, currently serving as an Assistant Professor at this institute. His expertise and teaching interests encompass a broad range of areas, including Power Systems, Power Electronics, and Electrical Machines. With a passion for advancing knowledge and fostering innovation, Dr. Khan is committed to contributing to the development of these critical fields in electrical engineering

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