Loading…
Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series
With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals...
Saved in:
Published in: | Sustainability 2024-11, Vol.16 (22), p.9770 |
---|---|
Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | cdi_FETCH-LOGICAL-c148t-74b71a305ca2aa0c6545817629c813025c6a964af33f644f46ac7440e85c61d3 |
container_end_page | |
container_issue | 22 |
container_start_page | 9770 |
container_title | Sustainability |
container_volume | 16 |
creator | Vallejo-Coral, E. Catalina Garzón, Ricardo Ortega López, Miguel Darío Martínez-Gómez, Javier Moya, Marcelo |
description | With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result. |
doi_str_mv | 10.3390/su16229770 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3133371872</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3133371872</sourcerecordid><originalsourceid>FETCH-LOGICAL-c148t-74b71a305ca2aa0c6545817629c813025c6a964af33f644f46ac7440e85c61d3</originalsourceid><addsrcrecordid>eNpNkF1LwzAUhoMoOOZu_AUB74Rq0qRJe6nVOWHgwOltidmJZrRNTVJ0_96MCXpuzsf7cM7hReickivGKnIdRiryvJKSHKFJTiTNKCnI8b_6FM1C2JIUjNGKign6voMIvrM94PgBeOWdsS0E7AxeuS_wuHZ9GLshWtdj26e268Brq1p8O9p2Y_v3sJ8r_Ap-hxcu4sXY2Q2uW9upCPglJCTJa-gG51VinsFbCGfoxKg2wOw3T9F6fr-uF9ny6eGxvllmmvIyZpK_SaoYKbTKlSJaFLwoqRR5pUvKSF5ooSrBlWHMCM4NF0pLzgmUSaEbNkUXh7WDd58jhNhs3ej7dLFhlDEmaSnzRF0eKO1dCB5MM_j0vt81lDR7b5s_b9kPiENrEQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3133371872</pqid></control><display><type>article</type><title>Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series</title><source>Publicly Available Content Database</source><source>Coronavirus Research Database</source><creator>Vallejo-Coral, E. Catalina ; Garzón, Ricardo ; Ortega López, Miguel Darío ; Martínez-Gómez, Javier ; Moya, Marcelo</creator><creatorcontrib>Vallejo-Coral, E. Catalina ; Garzón, Ricardo ; Ortega López, Miguel Darío ; Martínez-Gómez, Javier ; Moya, Marcelo</creatorcontrib><description>With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su16229770</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Case studies ; Clustering ; Cost control ; Decomposition ; Electricity ; Emissions ; Energy audits ; Energy consumption ; Energy management ; Green buildings ; HVAC ; Methods ; Smart meters ; Time series</subject><ispartof>Sustainability, 2024-11, Vol.16 (22), p.9770</ispartof><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c148t-74b71a305ca2aa0c6545817629c813025c6a964af33f644f46ac7440e85c61d3</cites><orcidid>0009-0006-1424-5799 ; 0000-0002-6370-9637 ; 0000-0001-8807-7595 ; 0000-0003-2065-0484</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3133371872/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3133371872?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,25731,27901,27902,36989,38493,43871,44566,74382,75096</link.rule.ids></links><search><creatorcontrib>Vallejo-Coral, E. Catalina</creatorcontrib><creatorcontrib>Garzón, Ricardo</creatorcontrib><creatorcontrib>Ortega López, Miguel Darío</creatorcontrib><creatorcontrib>Martínez-Gómez, Javier</creatorcontrib><creatorcontrib>Moya, Marcelo</creatorcontrib><title>Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series</title><title>Sustainability</title><description>With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result.</description><subject>Case studies</subject><subject>Clustering</subject><subject>Cost control</subject><subject>Decomposition</subject><subject>Electricity</subject><subject>Emissions</subject><subject>Energy audits</subject><subject>Energy consumption</subject><subject>Energy management</subject><subject>Green buildings</subject><subject>HVAC</subject><subject>Methods</subject><subject>Smart meters</subject><subject>Time series</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><recordid>eNpNkF1LwzAUhoMoOOZu_AUB74Rq0qRJe6nVOWHgwOltidmJZrRNTVJ0_96MCXpuzsf7cM7hReickivGKnIdRiryvJKSHKFJTiTNKCnI8b_6FM1C2JIUjNGKign6voMIvrM94PgBeOWdsS0E7AxeuS_wuHZ9GLshWtdj26e268Brq1p8O9p2Y_v3sJ8r_Ap-hxcu4sXY2Q2uW9upCPglJCTJa-gG51VinsFbCGfoxKg2wOw3T9F6fr-uF9ny6eGxvllmmvIyZpK_SaoYKbTKlSJaFLwoqRR5pUvKSF5ooSrBlWHMCM4NF0pLzgmUSaEbNkUXh7WDd58jhNhs3ej7dLFhlDEmaSnzRF0eKO1dCB5MM_j0vt81lDR7b5s_b9kPiENrEQ</recordid><startdate>20241108</startdate><enddate>20241108</enddate><creator>Vallejo-Coral, E. Catalina</creator><creator>Garzón, Ricardo</creator><creator>Ortega López, Miguel Darío</creator><creator>Martínez-Gómez, Javier</creator><creator>Moya, Marcelo</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PKEHL</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0009-0006-1424-5799</orcidid><orcidid>https://orcid.org/0000-0002-6370-9637</orcidid><orcidid>https://orcid.org/0000-0001-8807-7595</orcidid><orcidid>https://orcid.org/0000-0003-2065-0484</orcidid></search><sort><creationdate>20241108</creationdate><title>Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series</title><author>Vallejo-Coral, E. Catalina ; Garzón, Ricardo ; Ortega López, Miguel Darío ; Martínez-Gómez, Javier ; Moya, Marcelo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c148t-74b71a305ca2aa0c6545817629c813025c6a964af33f644f46ac7440e85c61d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Case studies</topic><topic>Clustering</topic><topic>Cost control</topic><topic>Decomposition</topic><topic>Electricity</topic><topic>Emissions</topic><topic>Energy audits</topic><topic>Energy consumption</topic><topic>Energy management</topic><topic>Green buildings</topic><topic>HVAC</topic><topic>Methods</topic><topic>Smart meters</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vallejo-Coral, E. Catalina</creatorcontrib><creatorcontrib>Garzón, Ricardo</creatorcontrib><creatorcontrib>Ortega López, Miguel Darío</creatorcontrib><creatorcontrib>Martínez-Gómez, Javier</creatorcontrib><creatorcontrib>Moya, Marcelo</creatorcontrib><collection>CrossRef</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vallejo-Coral, E. Catalina</au><au>Garzón, Ricardo</au><au>Ortega López, Miguel Darío</au><au>Martínez-Gómez, Javier</au><au>Moya, Marcelo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series</atitle><jtitle>Sustainability</jtitle><date>2024-11-08</date><risdate>2024</risdate><volume>16</volume><issue>22</issue><spage>9770</spage><pages>9770-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su16229770</doi><orcidid>https://orcid.org/0009-0006-1424-5799</orcidid><orcidid>https://orcid.org/0000-0002-6370-9637</orcidid><orcidid>https://orcid.org/0000-0001-8807-7595</orcidid><orcidid>https://orcid.org/0000-0003-2065-0484</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2071-1050 |
ispartof | Sustainability, 2024-11, Vol.16 (22), p.9770 |
issn | 2071-1050 2071-1050 |
language | eng |
recordid | cdi_proquest_journals_3133371872 |
source | Publicly Available Content Database; Coronavirus Research Database |
subjects | Case studies Clustering Cost control Decomposition Electricity Emissions Energy audits Energy consumption Energy management Green buildings HVAC Methods Smart meters Time series |
title | Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-22T18%3A32%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Determine%20the%20Profiles%20of%20Power%20Consumption%20in%20Commercial%20Buildings%20in%20a%20Very%20Hot%20Humid%20Climate%20Using%20a%20Temporary%20Series&rft.jtitle=Sustainability&rft.au=Vallejo-Coral,%20E.%20Catalina&rft.date=2024-11-08&rft.volume=16&rft.issue=22&rft.spage=9770&rft.pages=9770-&rft.issn=2071-1050&rft.eissn=2071-1050&rft_id=info:doi/10.3390/su16229770&rft_dat=%3Cproquest_cross%3E3133371872%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c148t-74b71a305ca2aa0c6545817629c813025c6a964af33f644f46ac7440e85c61d3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3133371872&rft_id=info:pmid/&rfr_iscdi=true |