Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31903
A Commercial Building Plug Load Management System That Uses Internet of Things Technology to Automatically Identify Plugged-In Devices and Their Locations

Authors: Amy LeBar, Kim L. Trenbath, Bennett Doherty, William Livingood

Abstract:

Plug and process loads (PPLs) account for a large portion of U.S. commercial building energy use. There is a huge potential to reduce whole building consumption by targeting PPLs for energy savings measures or implementing some form of plug load management (PLM). Despite this potential, there has yet to be a widely adopted commercial PLM technology. This paper describes the Automatic Type and Location Identification System (ATLIS), a PLM system framework with automatic and dynamic load detection (ADLD). ADLD gives PLM systems the ability to automatically identify devices as they are plugged into the outlets of a building. The ATLIS framework takes advantage of smart, connected devices to identify device locations in a building, meter and control their power, and communicate this information to a central database. ATLIS includes five primary capabilities: location identification, communication, control, energy metering, and data storage. A laboratory proof of concept (PoC) demonstrated all but the energy metering capability, and these capabilities were validated using a series of system tests. The PoC was able to identify when a device was plugged into an outlet and the location of the device in the building. When a device was moved, the PoC’s dashboard and database were automatically updated with the new location. The PoC implemented controls to devices from the system dashboard so that devices maintained correct schedules regardless of where they were plugged in within the building. ATLIS’s primary technology application is improved PLM, but other applications include asset management, energy audits, and interoperability for grid-interactive efficient buildings. An ATLIS-based system could also be used to direct power to critical devices, such as ventilators, during a brownout or blackout. Such a framework is an opportunity to make PLM more widespread and reduce the amount of energy consumed by PPLs in current and future commercial buildings.

Keywords: commercial buildings, grid-interactive efficient buildings, miscellaneous electric loads, plug loads, plug load management

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 417

References:


[1] U.S. Energy Information Administration, “Annual Energy Outlook 2020,” 2020.
[2] A. J. Kandt and M. R. Langner, “Plug Load Management System Field Study,” Golden, CO (United States), Feb. 2019, doi: 10.2172/1495720.
[3] M. R. Langner, R. Langner, and T.-K. L. Trenbath, “Integrating Smart Plug and Process Load Controls into Energy Management Information System Platforms: A Landscaping Study,” Golden, CO (United States), Jun. 2019, doi: 10.2172/1530714.
[4] K. Trenbath, B. Doherty, K. Vrabel, and C. Burke, “Emerging Technologies for Improved Plug Load Management Systems: Learning Behavior Algorithms and Automatic and Dynamic Load Detection,” in ACEEE Summer Study on Energy Efficiency in Buildings, 2020. (Online). Available: https://betterbuildingssolutioncenter.energy.gov/sites/default/files/attachments/ACEEE_2020_Plug_Load_Mgmt_Paper.pdf.
[5] M. A. Stubbs and M. Roman, “Identification of Powered Devices for Energy Saving,” US 8461725B1, 2013.
[6] J. Allen, M. Deadman, S. Marland, and A. O’Neill, “Remote Control of Powering of Electrical Appliances,” US 9563792B2, 2017.
[7] L. A. Naaman, “Remotely Controllable Electrical Sockets with Plugged Appliance Detection and Identification,” US 9304947B2, 2016.
[8] S.-M. Chung, H.-H. Lee, and C.-C. Lee, “Smart Plugs, Smart Sockets and Smart Adaptors,” US 9231351B2, 2016.
[9] A. De Mauro, M. Greco, and M. Grimaldi, “What is big data? A consensual definition and a review of key research topics,” vol. 1644, p. 297, 2015, doi: 10.1063/1.4907823.
[10] Khaled Salah Mohamed, The Era of Internet of Things: Towards a Smart World. 2019.
[11] Google, “Nest & Google - The best of Google. The best of Nest.,” 2019. (Online). Available: https://store.google.com/us/category/google_nest?hl=en-US&GoogleNest&utm_source=nest_referral&utm_medium=google_oo&utm_campaign=GS102516. (Accessed: 16-Aug-2021).
[12] Apple, “Apple Home,” 2017. (Online). Available: https://www.apple.com/ios/home/. (Accessed: 16-Aug-2021).
[13] Amazon, “Amazon Alexa.” (Online). Available: https://developer.amazon.com/en-US/alexa. (Accessed: 16-Aug-2021).
[14] B. Nordman, M. Kloss, B. Kundu, N. Dewart, A. Prakash, L Wong, et al., “Energy Reporting: Device Demonstration, Communication Protocols, and Codes and Standards,” 2019.
[15] S. De Bruin, B. Ghena, Y. S. Kuo, and P. Dutta, “PowerBlade: A low-profile, true-power, plug-through energy meter,” in SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, 2015, pp. 17–29, doi: 10.1145/2809695.2809716.
[16] Z. Xiao, “An efficient Power over Ethernet (PoE) interface with current-balancing and hot-swapping control,” IEEE Trans. Ind. Electron., vol. 65, no. 3, pp. 2496–2506, Mar. 2018, doi: 10.1109/TIE.2017.2739693.
[17] I. U. Perera, D. R. Thotagamuwa, J. P. Freyssinier, and N. Narendran, “Characterization of a Power-over-Ethernet (PoE)-based LED lighting system,” no. April 2019, p. 56, 2019, doi: 10.1117/12.2510019.
[18] Legrand, “RAD A/C FAST CHARG USB + DUP 15A W.” (Online). Available: https://www.legrand.us/wiring-devices/outlets-and-receptacles/residential-receptacles/radiant-15a-tamper-resistant-ultra-fast-usb-type-a-c-outlet/p/r26usbac6w?gclid=Cj0KCQjw3f6HBhDHARIsAD_i3D_PEdiKhUe5497GIQJkfjr5dbtSIbkNETb-KMVnc_Pf2NCo-WgcH6saAhE1EALw_w. (Accessed: 16-Aug-2021).
[19] Leviton, “GUSB1-W - 15A SmartlockPro® GFCI Combination 24W(4.8A) Type A USB In-Wall Charger Outlet in White.” (Online). Available: https://www.leviton.com/en/products/gusb1-w. (Accessed: 16-Aug-2021).
[20] D. L. Gerber, V. Vossos, W. Feng, C. Marnay, B. Nordman, and R. Brown, “A simulation-based efficiency comparison of AC and DC power distribution networks in commercial buildings,” Appl. Energy, vol. 210, pp. 1167–1187, Jan. 2018, doi: 10.1016/j.apenergy.2017.05.179.
[21] “Arduino.” (Online). Available: https://www.arduino.cc/. (Accessed: 16-Aug-2021).
[22] ZigBee Alliance, “Connectivity Standards Alliance,” 2020. (Online). Available: https://csa-iot.org/. (Accessed: 16-Aug-2021).
[23] “Z-Wave.” (Online). Available: https://www.z-wave.com/. (Accessed: 16-Aug-2021).
[24] “Bluetooth® Technology Website.” (Online). Available: https://www.bluetooth.com/. (Accessed: 16-Aug-2021).
[25] M. Neukomm, V. Nubbe, and R. Fares, “Grid-interactive Efficient Buildings: Overview,” 2019, doi: 10.2172/1508212.
[26] F. Sehar, M. Pipattanasomporn, and S. Rahman, “Integrated automation for optimal demand management in commercial buildings considering occupant comfort,” Sustain. Cities Soc., vol. 28, pp. 16–29, Jan. 2017, doi: 10.1016/j.scs.2016.08.016.
[27] M. S. Hoosain and B. S. Paul, “Smart homes: A domestic demand response and demand side energy management system for future smart grids,” in Proceedings of the 25th Conference on the Domestic Use of Energy, DUE 2017, 2017, pp. 285–291, doi: 10.23919/DUE.2017.7931852.
[28] A. Saha, M. Kuzlu, M. Pipattanasomporn, and S. Rahman, “Enabling Residential Demand Response Applications with a ZigBee-Based Load Controller System,” Intell. Ind. Syst., vol. 2, no. 4, pp. 303–318, 2016, doi: 10.1007/s40903-016-0059-4.
[29] F. Sehar, “An Approach to Mitigate Electric Vehicle Penetration Challenges through Demand Response, Solar Photovoltaics and Energy Storage Applications in Commercial Buildings,” Virginia Polytechnic Institute and State University, Arlington, Virginia, 2017. (Online). Available: http://hdl.handle.net/10919/86654. (Accessed 16-Aug-2021).
[30] R. Yin, S. Kiliccote, and M. A. Piette, “Linking measurements and models in commercial buildings: A case study for model calibration and demand response strategy evaluation,” Energy Build., vol. 124, pp. 222–235, Jul. 2016, doi: 10.1016/j.enbuild.2015.10.042.
[31] ASHRAE, “BACnet,” 2014. (Online). Available: http://www.bacnet.org/. (Accessed: 16-Aug-2021).
[32] H. Bergmann, C. Mosiman, A. Saha, S. Haile, W. Livingood, S. Bushby, et al., “Semantic Interoperability to Enable Smart, Grid-Interactive Efficient Buildings,” in ACEEE Summer Study on Energy Efficiency in Buildings, 2020, doi: 10.20357/B7S304.
[33] Project Haystack, “Project Haystack - Tags,” 2020. (Online). Available: https://www.project-haystack.org/. (Accessed: 16-Aug-2021).
[34] BrickSchema, “BrickSchema.” (Online). Available: https://brickschema.org/. (Accessed: 16-Aug-2021).
[35] ASHRAE, “ASHRAE Titles, Purposes, and Scopes.” (Online). Available: https://www.ashrae.org/technical-resources/standards-and-guidelines/titles-purposes-and-scopes. (Accessed: 16-Aug-2021).
[36] B. Nordman and M. Sanchez, “Electronics Come of Age: A Taxonomy for Miscellaneous and Low Power Products,” in ACEEE Summer Study on Energy Efficiency in Buildings, 2006. (Online). Available: https://www.aceee.org/files/proceedings/2006/data/papers/SS06_Panel9_Paper22.pdf. (Accessed: 16-Aug-2021).
[37] J. Butzbaugh, R. Hosbach, and A. Meier, “Miscellaneous Electric Loads: Characterization and Energy Savings Potential,” Energy Build., 2021, doi: 10.1016/j.enbuild.2021.110892.
[38] K. R. Krishnan, H. D. Chinh, M. Gupta, S. K. Panda, and C. J. Spanos, “Context-Aware Plug-Load Identification Towards Enhanced Energy Efficiency in the Built Environment,” in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018, 2018, doi: 10.1109/EEEIC.2018.8494526.