Quantification of energy cost savings through optimization and control of appliances within smart neighborhood homes

Supriya Chinthavali, Varisara Tansakul, Sangkeun Lee, Anika Tabassum, Jeff Munk, Jan Jakowski, Michael Starke, Teja Kuruganti, Heather Buckberry, Jim Leverette

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Electric utilities are driving towards enabling automatic scheduling and control of the consumption pattern of appliances such as heating, ventilation, and air conditioning (HVAC) and water heater (WH) systems (e.g., through preheating and pre-cooling, etc.) within smart neighborhoods to minimize energy cost and peak load demand. Quantifying economic savings through direct comparison of the optimized energy usage profile on a specific day with the typical non-optimized usage profile on another day is not a fair comparison because energy usage highly depends on weather conditions and human behaviour especially appliance like HVAC on those days. In this paper, we propose a novel approach of identifying similar weather day pairs which can then be used to compare the energy use profiles within homes between the identified pairs. We then demonstrate how the proposed approach can be used to compute cost savings due to optimization and control of smart appliances at home and neighborhood-level within a future-focused smart neighborhood of 62 residential homes. We also demonstrate a simulation based approach to quantify cost savings and showcase our findings through customized and interactive visualizations.

Original languageEnglish
Title of host publicationUrbSys 2019 - Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization, Part of BuildSys 2019
PublisherAssociation for Computing Machinery, Inc
Pages59-68
Number of pages10
ISBN (Electronic)9781450370141
DOIs
StatePublished - Nov 13 2019
Event1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization, UrbSys 2019 - Part of BuildSys 2019 - New York, United States
Duration: Nov 10 2019 → …

Publication series

NameUrbSys 2019 - Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization, Part of BuildSys 2019

Conference

Conference1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization, UrbSys 2019 - Part of BuildSys 2019
Country/TerritoryUnited States
CityNew York
Period11/10/19 → …

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

  • Furnace
  • HVAC
  • Internet of Things
  • Sensors
  • Smart neighborhood
  • Water heater

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