research-article

Managing plug-loads for demand response within buildings

Authors:

Bharathan Balaji,

Seemanta Dutta,

Yuvraj AgarwalAuthors Info & Claims

BuildSys '11: Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings

Pages 13 - 18

https://doi.org/10.1145/2434020.2434024

Published: 01 November 2011 Publication History

Abstract

Detailed and accurate energy accounting is an important first step in improving energy efficiency within buildings. Based on this information, building managers can perform active energy management, especially during demand response situations that require load shedding over short time scales. While individual plug-loads are an important target for demand response, they pose significant challenges due to their distributed nature and the significant diversity of devices that are deployed.

This paper presents the design and implementation of our energy accounting and management system which is specifically geared towards managing plug-loads within enterprise buildings. Our system provides fine-grained visibility and control of plug-loads to building managers, allowing them to deal with demand response situations through user-specified actuation policies. At its core, our system consists of our wireless smart energy meter with actuation capabilities, ZigBee-based wireless network infrastructure, and our Demand Response Server, an analysis engine that provides interfaces for initiating load-shedding policies. Our micro-benchmarks show the different methods that building managers can utilize to efficiently manage devices during demand response events.

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Cited By

Yin YXu BZhong YBao TWang P(2023)A Non-Intrusive Load Monitoring Model for Electric Vehicles Based on Multi-Kernel Conventional Neural NetworkWorld Electric Vehicle Journal10.3390/wevj1402005114:2(51)Online publication date: 10-Feb-2023
https://doi.org/10.3390/wevj14020051
Al-Ghaili AIbrahim ZBakar AKasim HAl-Hada NJørgensen BHassan ZOthman MKasmani RShayea I(2023)A Systematic Review on Demand Response Role Toward Sustainable Energy in the Smart Grids-Adopted Buildings SectorIEEE Access10.1109/ACCESS.2023.328764111(64968-65027)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3287641
Manzano-Agugliaro FChihib MChourak MMartínez JZapata-Sierra AAlcayde A(2023)Monitoring energy consumption of vending machines in university buildingsEnergy Reports10.1016/j.egyr.2023.09.17710(3252-3262)Online publication date: Nov-2023
https://doi.org/10.1016/j.egyr.2023.09.177
Show More Cited By

Index Terms

Managing plug-loads for demand response within buildings
1. Applied computing
  1. Operations research
    1. Industry and manufacturing
      1. Command and control
2. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

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Published In

cover image ACM Conferences

BuildSys '11: Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings

November 2011

87 pages

ISBN:9781450307499

DOI:10.1145/2434020

General Chair:
Antonio Ruzzelli
University College, Dublin, Ireland

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 November 2011

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SenSys '11

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SenSys '11: The 9th ACM Conference on Embedded Network Sensor Systems

November 1, 2011

Washington, Seattle

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Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

Yin YXu BZhong YBao TWang P(2023)A Non-Intrusive Load Monitoring Model for Electric Vehicles Based on Multi-Kernel Conventional Neural NetworkWorld Electric Vehicle Journal10.3390/wevj1402005114:2(51)Online publication date: 10-Feb-2023
https://doi.org/10.3390/wevj14020051
Al-Ghaili AIbrahim ZBakar AKasim HAl-Hada NJørgensen BHassan ZOthman MKasmani RShayea I(2023)A Systematic Review on Demand Response Role Toward Sustainable Energy in the Smart Grids-Adopted Buildings SectorIEEE Access10.1109/ACCESS.2023.328764111(64968-65027)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3287641
Manzano-Agugliaro FChihib MChourak MMartínez JZapata-Sierra AAlcayde A(2023)Monitoring energy consumption of vending machines in university buildingsEnergy Reports10.1016/j.egyr.2023.09.17710(3252-3262)Online publication date: Nov-2023
https://doi.org/10.1016/j.egyr.2023.09.177
Shi JPan ZJiang LZhai X(2023)An ontology-based methodology to establish city information model of digital twin city by merging BIM, GIS and IoTAdvanced Engineering Informatics10.1016/j.aei.2023.10211457:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.aei.2023.102114
Jain MKundu SBhattacharya AHuang SChandan VRadhakrishnan NAdetola VVrabie D(2021)Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads2021 American Control Conference (ACC)10.23919/ACC50511.2021.9482639(4171-4177)Online publication date: 25-May-2021
https://doi.org/10.23919/ACC50511.2021.9482639
Fraternali FBalaji BHong DAgarwal YGupta RJiang XGnawali ONagy Z(2021)MarbleProceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3486611.3486670(140-149)Online publication date: 17-Nov-2021
https://dl.acm.org/doi/10.1145/3486611.3486670
Trivedi DBadarla V(2019)Occupancy detection systems for indoor environments: A survey of approaches and methodsIndoor and Built Environment10.1177/1420326X1987562129:8(1053-1069)Online publication date: 16-Sep-2019
https://doi.org/10.1177/1420326X19875621
Nacci ARana VBalaji BSpoletini PGupta RSciuto DAgarwal Y(2018)BuildingRulesACM Transactions on Cyber-Physical Systems10.1145/31855002:2(1-22)Online publication date: 23-May-2018
https://dl.acm.org/doi/10.1145/3185500
Hviid JKjargaard M(2018)Service Abstraction Layer for Building Operating Systems: Enabling portable applications and improving system resilience2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)10.1109/SmartGridComm.2018.8587543(1-6)Online publication date: Oct-2018
https://doi.org/10.1109/SmartGridComm.2018.8587543
Sonta ASimmons PJain R(2018)Understanding building occupant activities at scaleAdvanced Engineering Informatics10.1016/j.aei.2018.04.00937:C(1-13)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1016/j.aei.2018.04.009
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