A new approach of electrical appliance identification in residential buildings

Abstract This paper proposes a simple algorithm about non-intrusive appliance load monitoring (NIALM) method. The main objective is to analyze the overall power consumption of a given building and to identify the different operating appliances. This approach aims to reduce the overall energy expense of maintaining a specified level of comfort. In our approach, we firstly replace the main signal by a shorter form in order to reduce computing time. This criterion is important to guarantee real-time operation mode. Furthermore, we can classify the operating devices through their type and the mean electrical power consumed. Finally, for identification, we use the template’s waveform matching to identify the individual energy consumption with an optimized manner. To validate the proposed algorithm, satisfactory simulation results showing the reliability of the proposed NIALM method are found.

[1]  Radu Zmeureanu,et al.  Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses , 1999 .

[2]  Xu Chen,et al.  Cost-Effective and Privacy-Preserving Energy Management for Smart Meters , 2015, IEEE Transactions on Smart Grid.

[3]  Said Drid,et al.  Management, optimization and conversion of energy for self-governing house , 2017, 2017 International Conference on Control, Automation and Diagnosis (ICCAD).

[4]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[5]  James L. Kirtley,et al.  Non-intrusive induction motor speed detection , 2015 .

[6]  Muhammad Ali Imran,et al.  Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey , 2012, Sensors.

[7]  Sousso Kelouwani,et al.  Non-intrusive load monitoring through home energy management systems: A comprehensive review , 2017 .

[8]  M. Guedri,et al.  Caractérisation aveugle de la courbe de charge électrique : Détection, classification et estimation des usages dans les secteurs résidentiel et tertiaire , 2009 .

[9]  Mark Lucente,et al.  Exploration on Load Signatures , 2004 .

[10]  Ali Badri,et al.  A survey on mobile energy storage systems (MESS): Applications, challenges and solutions , 2014 .

[11]  Bernardete Ribeiro,et al.  An Experimental Study on Electrical Signature Identification of Non-Intrusive Load Monitoring (NILM) Systems , 2011, ICANNGA.

[12]  Silvia Santini,et al.  The ECO data set and the performance of non-intrusive load monitoring algorithms , 2014, BuildSys@SenSys.

[13]  Hussain Shareef,et al.  Application of load monitoring in appliances’ energy management – A review , 2017 .

[14]  K. Agbossou,et al.  A semi-synthetic dataset development tool for household energy consumption analysis , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[15]  Loi Lei Lai,et al.  Harmonics load signature recognition by wavelets transforms , 2000, DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382).

[16]  Jean-Charles Le Bunetel,et al.  High accuracy event detection for Non-Intrusive Load Monitoring , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Lucio Soibelman,et al.  User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings , 2011 .

[18]  Fangxing Li,et al.  A Smart Home Test Bed for Undergraduate Education to Bridge the Curriculum Gap From Traditional Power Systems to Modernized Smart Grids , 2015, IEEE Transactions on Education.

[19]  Scott Dick,et al.  Toward Non-Intrusive Load Monitoring via Multi-Label Classification , 2017, IEEE Transactions on Smart Grid.

[20]  A. Longjun Wang,et al.  Non-intrusive load monitoring algorithm based on features of V–I trajectory , 2018 .

[21]  Won-Hwa Hong,et al.  Constructing electricity load profile and formulating load pattern for urban apartment in Korea , 2014 .

[22]  Hala Najmeddine Méthode d'identification et de classification de la consommation d'énergie par usages en vue de l'intégration dans un compteur d'énergie électrique , 2009 .

[23]  Steven B. Leeb,et al.  Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms , 1996 .

[24]  Michael Zeifman,et al.  Nonintrusive appliance load monitoring: Review and outlook , 2011, IEEE Transactions on Consumer Electronics.

[25]  Gaël Richard,et al.  A Generative Model for Non-Intrusive Load Monitoring in Commercial Buildings , 2018, Energy and Buildings.

[26]  Steven B. Leeb,et al.  Smart Metering of Variable Power Loads , 2015, IEEE Transactions on Smart Grid.

[27]  Xinghuo Yu,et al.  Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey , 2016, IEEE Transactions on Industrial Informatics.

[28]  Hsueh-Hsien Chang,et al.  Load identification in nonintrusive load monitoring using steady-state and turn-on transient energy algorithms , 2010, The 2010 14th International Conference on Computer Supported Cooperative Work in Design.

[29]  Said Drid,et al.  Dynamic control and advanced load management of a stand-alone hybrid renewable power system for remote housing , 2015 .