Smart Home Electric Energy Management Using Non-Intrusive Appliance Load Monitoring (NILM)

Nurman Hariyanto, Dian Anggraini, Ary Setijadi Prihatmanto

Abstract


Reducing the use of electrical energy in everyday life can be done with the awareness of the user. Awareness of using electrical energy can be done by providing information about the use of electricity itself. In developing a smart home with energy management systems or other commercial electronic devices, a tool that can measure or sort electricity usage in buildings and households is needed based on current and voltage units. Measuring and sorting what is meant is separating the total power consumption used as a load of a specific device that can be used by applying the Non-Intrusive Load Monitoring (NILM) technique known as Energy Disaggregation. The results are shown by NILM using the IoT concept data will be sent to the server via the internet using Message Queuing Telemetry Transport (MQTT). The data is processed and given to the user in the form of measurement results for each electronic device connected to the measuring device. From these results, the system can separate the energy from the refrigerator and air conditioner from the total energy consumed at one time. This step is one way to make energy efficient, that an energy management system with iot concept is built.

Keywords


Internet of things; Non-intrusive load monitoring; Smart home energy management systems

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References


Zoha, A., Gluhak, A., Imran, M. A., & Rajasegarar, S. (2012). Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors, 12(12), 16838-16866.Berges, M.; Goldman, E.;

Matthews, H.S.; Soibelman, L.; and Anderson, K. (2011) User-centered non-intrusive electricity load monitoring for residential buildings. j. comput. Civil Eng, 25, 471–480.

Carrie Armel, K.; Gupta, A.; Shrimali, G.; Albert, A. (2013). Is disaggregation the holy grail of energy efficiency? the case of electricity. Energ. Policy 52, 213–234.

Egarter, D., & Elmenreich, W. (2015, March). Autonomous load disaggregation approach based on active power measurements. In 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) (pp. 293-298). IEEE.

Ehrhardt-Martinez, K.; Donnelly, K.A.; and Laitner (2010)., J.A.Advanced metering initiatives and residential feedback programs: a meta-review for household electricity-saving opportunities; Technical Report E105 for American Council for an Energy-Efficient Economy (ACEE): Washington, DC, USA.

Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12), 1870-1891.Guizien Martin (2021). Desagregación del consumo eléctrico e identificación de dispositivos en hogares en el contexto de monitorización no intrusiva (NILM) utilizando Deep Learning. repositorio.comillas.edu

J Ranjan. Object user recognition in multi-person homes (2015). UbiComp/ISWC'15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers

J Raya & J CerdaA (2014). Low frequency power-based event alignment method for trained NILM systems. IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) DOI: 10.1109/ROPEC34556.2014

L Ma, P Schegner (2019). State duration-based event detection for domestic power disaggregation. IEEE Milan PowerTech

Ghosh, S., Chatterjee, A., & Chatterjee, D. (2021). An improved load feature extraction technique for smart Homes using fuzzy-based NILM. IEEE Transactions on Instrumentation and Measurement, 70, 1-9.

Jürgel, M., & Mass, J. (2019). Non-Intrusive Load Monitoring in the OpenHAB smart home framework. A bachelor’s thesis of University of Tartu.Youssef, T. (2017). Co-design of Security Aware Power System Distribution Architecture as Cyber Physical System.

Iqbal, J., Khan, M., Talha, M., Farman, H., Jan, B., Muhammad, A., & Khattak, H. A. (2018). A generic internet of things architecture for controlling electrical energy consumption in smart homes. Sustainable cities and society, 43, 443-450. 1-39

Del Mar, P. (2013). Motor load detection for voltage transient based non-intrusive load monitoring. Doctoral dissertation, University of Delaware.

Khanchuea, K., & Siripokarpirom, R. (2019, March). A multi-protocol IoT gateway and WiFi/BLE sensor nodes for smart home and building automation: Design and implementation. In 2019 10th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES) (pp. 1-6). IEEE.

Mauceli, E., Geng, Z. K., Hamilton, W. O., Johnson, W. W., Merkowitz, S., Morse, A., ... & Solomonson, N. (1996). The Allegro gravitational wave detector: Data acquisition and analysis. Physical Review D, 54(2), 1264.




DOI: https://doi.org/10.17509/seict.v2i1.34572

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