Loading…
Efficient Management of Building Energy Resources
Heating, ventilation, and air-conditioning costs dominate the overall energy bill of commercial buildings. These costs are even higher in developing countries where diesel generators provide backup power during recurrent power outages. In this work, we propose two efficient approaches to minimize en...
Saved in:
Main Authors: | , |
---|---|
Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | 63 |
container_issue | |
container_start_page | 56 |
container_title | |
container_volume | |
creator | Saurav, Kumar Arya, Vijay |
description | Heating, ventilation, and air-conditioning costs dominate the overall energy bill of commercial buildings. These costs are even higher in developing countries where diesel generators provide backup power during recurrent power outages. In this work, we propose two efficient approaches to minimize energy costs associated with HVAC systems in the presence of outages and a mix of supply resources including grid, DG, solar and batteries. First, we develop an MILP optimization framework to solve the problem optimally. The framework uses novel relaxed HVAC models that speed-up computation without compromising solution accuracy. Thereafter, we develop a fast heuristic algorithm which uses a derived closed form solution for the optimal pre-cooling time and provides near optimal solutions in about three orders of magnitude less time compared to the optimization framework. Our experimental results demonstrate that both approaches yield average savings of 20% relative to the baseline operational practices in office buildings and cell towers. |
doi_str_mv | 10.1109/COMSNETS.2019.8711105 |
format | conference_proceeding |
fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_8711105</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8711105</ieee_id><sourcerecordid>8711105</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-6725c5c869f9980b3301c522e63faf78564000b0537ddb6108c3d3d07c2704f43</originalsourceid><addsrcrecordid>eNotj81qwkAUhacFoWLzBKWQF0h679zc-Vm2If0BraB2LclkJkzRWBJd-Pa11NU5fIvDd4R4RMgRwT6Vy8X6s9qscwloc6PxQvlGJFYbZDJKW5D2VkwlMmeSwd6JZBy_AYDQWCY5FViFEF30_TFd1H3d-f1fPYT05RR3bey7tOr90J3TlR8Pp8H58V5MQr0bfXLNmfh6rTblezZfvn2Uz_MsouZjprRkx84oG6w10BABOpbSKwp10IZVcfFogEm3baMQjKOWWtBOaihCQTPx8L8bvffbnyHu6-G8vZ6kX2FYRHI</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Efficient Management of Building Energy Resources</title><source>IEEE Xplore All Conference Series</source><creator>Saurav, Kumar ; Arya, Vijay</creator><creatorcontrib>Saurav, Kumar ; Arya, Vijay</creatorcontrib><description>Heating, ventilation, and air-conditioning costs dominate the overall energy bill of commercial buildings. These costs are even higher in developing countries where diesel generators provide backup power during recurrent power outages. In this work, we propose two efficient approaches to minimize energy costs associated with HVAC systems in the presence of outages and a mix of supply resources including grid, DG, solar and batteries. First, we develop an MILP optimization framework to solve the problem optimally. The framework uses novel relaxed HVAC models that speed-up computation without compromising solution accuracy. Thereafter, we develop a fast heuristic algorithm which uses a derived closed form solution for the optimal pre-cooling time and provides near optimal solutions in about three orders of magnitude less time compared to the optimization framework. Our experimental results demonstrate that both approaches yield average savings of 20% relative to the baseline operational practices in office buildings and cell towers.</description><identifier>EISSN: 2155-2509</identifier><identifier>EISBN: 9781538679029</identifier><identifier>EISBN: 1538679027</identifier><identifier>DOI: 10.1109/COMSNETS.2019.8711105</identifier><language>eng</language><publisher>IEEE</publisher><subject>Batteries ; Buildings ; Computational complexity ; HVAC ; Load modeling ; Optimization ; Partial discharges</subject><ispartof>2019 11th International Conference on Communication Systems & Networks (COMSNETS), 2019, p.56-63</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8711105$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8711105$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Saurav, Kumar</creatorcontrib><creatorcontrib>Arya, Vijay</creatorcontrib><title>Efficient Management of Building Energy Resources</title><title>2019 11th International Conference on Communication Systems & Networks (COMSNETS)</title><addtitle>COMSNETS</addtitle><description>Heating, ventilation, and air-conditioning costs dominate the overall energy bill of commercial buildings. These costs are even higher in developing countries where diesel generators provide backup power during recurrent power outages. In this work, we propose two efficient approaches to minimize energy costs associated with HVAC systems in the presence of outages and a mix of supply resources including grid, DG, solar and batteries. First, we develop an MILP optimization framework to solve the problem optimally. The framework uses novel relaxed HVAC models that speed-up computation without compromising solution accuracy. Thereafter, we develop a fast heuristic algorithm which uses a derived closed form solution for the optimal pre-cooling time and provides near optimal solutions in about three orders of magnitude less time compared to the optimization framework. Our experimental results demonstrate that both approaches yield average savings of 20% relative to the baseline operational practices in office buildings and cell towers.</description><subject>Batteries</subject><subject>Buildings</subject><subject>Computational complexity</subject><subject>HVAC</subject><subject>Load modeling</subject><subject>Optimization</subject><subject>Partial discharges</subject><issn>2155-2509</issn><isbn>9781538679029</isbn><isbn>1538679027</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2019</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj81qwkAUhacFoWLzBKWQF0h679zc-Vm2If0BraB2LclkJkzRWBJd-Pa11NU5fIvDd4R4RMgRwT6Vy8X6s9qscwloc6PxQvlGJFYbZDJKW5D2VkwlMmeSwd6JZBy_AYDQWCY5FViFEF30_TFd1H3d-f1fPYT05RR3bey7tOr90J3TlR8Pp8H58V5MQr0bfXLNmfh6rTblezZfvn2Uz_MsouZjprRkx84oG6w10BABOpbSKwp10IZVcfFogEm3baMQjKOWWtBOaihCQTPx8L8bvffbnyHu6-G8vZ6kX2FYRHI</recordid><startdate>201901</startdate><enddate>201901</enddate><creator>Saurav, Kumar</creator><creator>Arya, Vijay</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201901</creationdate><title>Efficient Management of Building Energy Resources</title><author>Saurav, Kumar ; Arya, Vijay</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-6725c5c869f9980b3301c522e63faf78564000b0537ddb6108c3d3d07c2704f43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Batteries</topic><topic>Buildings</topic><topic>Computational complexity</topic><topic>HVAC</topic><topic>Load modeling</topic><topic>Optimization</topic><topic>Partial discharges</topic><toplevel>online_resources</toplevel><creatorcontrib>Saurav, Kumar</creatorcontrib><creatorcontrib>Arya, Vijay</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Saurav, Kumar</au><au>Arya, Vijay</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Efficient Management of Building Energy Resources</atitle><btitle>2019 11th International Conference on Communication Systems & Networks (COMSNETS)</btitle><stitle>COMSNETS</stitle><date>2019-01</date><risdate>2019</risdate><spage>56</spage><epage>63</epage><pages>56-63</pages><eissn>2155-2509</eissn><eisbn>9781538679029</eisbn><eisbn>1538679027</eisbn><abstract>Heating, ventilation, and air-conditioning costs dominate the overall energy bill of commercial buildings. These costs are even higher in developing countries where diesel generators provide backup power during recurrent power outages. In this work, we propose two efficient approaches to minimize energy costs associated with HVAC systems in the presence of outages and a mix of supply resources including grid, DG, solar and batteries. First, we develop an MILP optimization framework to solve the problem optimally. The framework uses novel relaxed HVAC models that speed-up computation without compromising solution accuracy. Thereafter, we develop a fast heuristic algorithm which uses a derived closed form solution for the optimal pre-cooling time and provides near optimal solutions in about three orders of magnitude less time compared to the optimization framework. Our experimental results demonstrate that both approaches yield average savings of 20% relative to the baseline operational practices in office buildings and cell towers.</abstract><pub>IEEE</pub><doi>10.1109/COMSNETS.2019.8711105</doi><tpages>8</tpages></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | EISSN: 2155-2509 |
ispartof | 2019 11th International Conference on Communication Systems & Networks (COMSNETS), 2019, p.56-63 |
issn | 2155-2509 |
language | eng |
recordid | cdi_ieee_primary_8711105 |
source | IEEE Xplore All Conference Series |
subjects | Batteries Buildings Computational complexity HVAC Load modeling Optimization Partial discharges |
title | Efficient Management of Building Energy Resources |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T16%3A49%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Efficient%20Management%20of%20Building%20Energy%20Resources&rft.btitle=2019%2011th%20International%20Conference%20on%20Communication%20Systems%20&%20Networks%20(COMSNETS)&rft.au=Saurav,%20Kumar&rft.date=2019-01&rft.spage=56&rft.epage=63&rft.pages=56-63&rft.eissn=2155-2509&rft_id=info:doi/10.1109/COMSNETS.2019.8711105&rft.eisbn=9781538679029&rft.eisbn_list=1538679027&rft_dat=%3Cieee_CHZPO%3E8711105%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-6725c5c869f9980b3301c522e63faf78564000b0537ddb6108c3d3d07c2704f43%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=8711105&rfr_iscdi=true |