Publication
New Data Center Performance Index: Perfect Design Data Center - PDD
| dc.contributor.author | Santos, Alexandre Fernandes | |
| dc.contributor.author | Gaspar, Pedro Dinis | |
| dc.contributor.author | Souza, Heraldo | |
| dc.date.accessioned | 2022-04-01T10:35:23Z | |
| dc.date.available | 2022-04-01T10:35:23Z | |
| dc.date.issued | 2020-10-04 | |
| dc.description.abstract | Data Centers (DC) are specific buildings that require large infrastructures to store all the information needed by companies. All data transmitted over the network is stored on CDs. By the end of 2020, Data Centers will grow 53% worldwide. There are methodologies that measure the e ciency of energy consumption. The most used metric is the Power Usage E ectiveness (PUE) index, but it does not fully reflect e ciency. Three DC’s located at the cities of Curitiba, Londrina and Iguaçu Falls (Brazil) with close PUE values, are evaluated in this article using the Energy Usage E ectiveness Design (EUED) index as an alternative to the current method. EUED uses energy as a comparative element in the design phase. Infrastructure consumption is the sum of energy with Heating, Ventilating and Air conditioning (HVAC) equipment, equipment, lighting and others. The EUED values obtained were 1.245 (kWh/yr)/(kWh/yr), 1.313 (kWh/yr)/(kWh/yr) and 1.316 (kWh/yr)/(kWh/yr) to Curitiba, Londrina and Iguaçu Falls, respectively. The di erence between the EUED and the PUE Constant External Air Temperature (COA) is 16.87% for Curitiba, 13.33% for Londrina and 13.30% for Iguaçu Falls. The new Perfect Design Data center (PDD) index prioritizes e ciency in increasing order is an easy index to interpret. It is a redefinition of EUED, given by a linear equation, which provides an approximate result and uses a classification table. It is a decision support index for the location of a Data Center in the project phase. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.3390/cli8100110 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.6/12143 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.relation.publisherversion | https://www.mdpi.com/2225-1154/8/10/110 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Data Center | pt_PT |
| dc.subject | PDD | pt_PT |
| dc.subject | PUE | pt_PT |
| dc.subject | Cooling | pt_PT |
| dc.subject | EUED | pt_PT |
| dc.subject | Performance index | pt_PT |
| dc.title | New Data Center Performance Index: Perfect Design Data Center - PDD | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 10 | pt_PT |
| oaire.citation.startPage | 110 | pt_PT |
| oaire.citation.title | Climate | pt_PT |
| oaire.citation.volume | 8 | pt_PT |
| person.familyName | Fernandes Santos | |
| person.familyName | Gaspar | |
| person.familyName | Souza | |
| person.givenName | Alexandre | |
| person.givenName | Pedro Dinis | |
| person.givenName | Heraldo | |
| person.identifier.ciencia-id | 6111-9F05-2916 | |
| person.identifier.orcid | 0000-0001-5306-6968 | |
| person.identifier.orcid | 0000-0003-1691-1709 | |
| person.identifier.orcid | 0000-0002-8471-7804 | |
| person.identifier.rid | N-3016-2013 | |
| person.identifier.scopus-author-id | 57419570900 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | dc73b2e8-4a09-433a-8015-cb876d3d2da0 | |
| relation.isAuthorOfPublication | b69e2ba0-43af-4cf7-873e-090fd9fc6c94 | |
| relation.isAuthorOfPublication | 7daa9e1e-c336-42c1-ae21-d3161ed1c7bc | |
| relation.isAuthorOfPublication.latestForDiscovery | b69e2ba0-43af-4cf7-873e-090fd9fc6c94 |
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