Low Complexity Energy Disaggregation Algorithm for Non-intrusive Load Monitoring

Abstract

Energy Disaggregation is the efficient technique to detect the energy profile of individual electric load by disaggregating the overall power consumption. The benefits of energy disaggregation are not only limited to residents but also helps to improve the building efficiency through load identification process. The idea of this paper is to provide a widespread review of energy disaggregation and present a scheme for non-intrusive load monitoring at a building level model. As the scheme involves a simple regression model with better accuracy, it is less complex to achieve energy disaggregation for the real time load identification in educational institution. Various metrics involved in the assessment of model performance like accuracy, standard errors values and computation time were evaluated and demonstrated for validity. A broad investigation is done for building level energy disaggregation and probable elucidations were discussed for future research initiation.

Keywords:

• non-intrusive load monitoring (NILM)
• energy management
• machine learning algorithms
• load identification
• power disaggregation

AUTHORS’ CONTRIBUTIONS

All authors are contributed equally to this work.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

No participation of humans takes place in this implementation process.

FUNDING

No funding is involved in this work.

HUMAN AND ANIMAL RIGHTS

No violation of Human and Animal Rights is involved.

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Additional information

Notes on contributors

P. M. Devie

P. M. Devie is working as an Assistant Professor in Thiagarajar College of Engineering, Madurai, India. She received her bachelor’s degree in Electrical and Electronics Engineering from KLN College of Information Technology, Madurai, in the year 2012. She completed her Masters Degree in Power Systems Engineering from Kamaraj College of Engineering & Technology, Madurai, in the year 2014. Her research interests are power system stability, data mining techniques for power system classification problems, non-intrusive load monitoring, smart grid technologies and its applications.

S. Kalyani

S. Kalyani has Completed UG Degree in the discipline of Electrical and Electronics Engineering in Alagappa Cheittar College of Engg. & Tech., Karaikudi in the year 2000 and completed her Post Graduation in the stream of Power Systems Engineering at Thiagarajar College of Engineering, Madurai in the year 2002. Also she was awarded with Ph.D. degree from Indian Institute of Technology Madras (IITM), Chennai in the year 2011. She has about 20 years of teaching experience at various levels and serving as Professor in the Department of EEE at Kamaraj College of Engineering and Technology. Her research areas of interest include Power System Dynamics and Stability, Machine learning techniques, Pattern Recognition, Artificial Intelligence. She is a Life member of ISTE, member of IE (India), member of IAEng and also a member of IEEE.

P. S. Manoharan

P. S. Manoharan is working as Professor in Thiagarajar College of Engineering, Madurai, India. He has completed his UG in EEE and PG in Power System Engineering from Thiagarajar College of Engineering, Madurai and Received Ph.D. degree from Anna University, Chennai, India. He has published more than 150 papers in International Journals and Conferences. His research interests include Solar energy, Power system management and Evolutionary computation.

V. Chandra

V. Chandra is working as an Assistant Professor at AAA College of Engineering and Technology, Sivakasi, India. She has completed her UG degree in EEE and PG degree in Applied Electronics from Madurai Kamaraj University, Madurai, India, and Anna University, Chennai, India respectively. She has 17 years of teaching experience in various engineering colleges. Currently she is pursuing her Ph.D. at Anna University, Chennai, India. Her research interests include renewable energy systems and electrical machines.