Ontology type: schema:ScholarlyArticle Open Access: True
2018-07
AUTHORSShouxiang WANG, Leijiao GE, Shengxia CAI, Lei WU
ABSTRACTSmart electricity utilization (SEU) is one of the most important components in a smart grid. It is crucial to evaluate efficiency, safety, and demand response capability of electricity users to achieve the smart use of electricity. The analytic hierarchy process (AHP) uses subjective criteria to determine index weights in multi-criteria decision-making problems, while the entropy method provides objectivity in determining index weights. Taking into account the uncertainty of expert scoring and user data, a hybrid interval analytic hierarchy process (IAHP) and interval entropy (IE) method is proposed for electricity user evaluation (EUE). Specifically, in the proposed method, electricity users are evaluated in terms of energy efficiency, safety monitoring, and demand response. The weights of EUE indices are calculated under uncertainty. The proposed approach derives subjective weights of EUE indices by the IAHP with expert scoring as input data, and determines objective weights of EUE indices by the IE method with user data as inputs. In order to obtain the optimal combined index weights, the two weights are normalized by a selected weight factor. Numerical case studies illustrate the effectiveness and advantages of the proposed approach, which combines subjective and objective information to derive the optimal combined index weights. More... »
PAGES701-711
http://scigraph.springernature.com/pub.10.1007/s40565-017-0355-3
DOIhttp://dx.doi.org/10.1007/s40565-017-0355-3
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1100075323
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1402",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Applied Economics",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Economics",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Tianjin University",
"id": "https://www.grid.ac/institutes/grid.33763.32",
"name": [
"Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, 300072, Tianjin, China"
],
"type": "Organization"
},
"familyName": "WANG",
"givenName": "Shouxiang",
"id": "sg:person.010724447613.83",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010724447613.83"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Tianjin University",
"id": "https://www.grid.ac/institutes/grid.33763.32",
"name": [
"Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, 300072, Tianjin, China"
],
"type": "Organization"
},
"familyName": "GE",
"givenName": "Leijiao",
"id": "sg:person.012042557551.59",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012042557551.59"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Nankai University",
"id": "https://www.grid.ac/institutes/grid.216938.7",
"name": [
"Zhou Enlai School of Government, Nankai University, 300071, Tianjin, China"
],
"type": "Organization"
},
"familyName": "CAI",
"givenName": "Shengxia",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Clarkson University",
"id": "https://www.grid.ac/institutes/grid.254280.9",
"name": [
"Electrical and Computer Engineering Department, Clarkson University, 13699, Potsdam, NY, USA"
],
"type": "Organization"
},
"familyName": "WU",
"givenName": "Lei",
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/j.ymssp.2014.05.036",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000713538"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.enconman.2013.12.034",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001910632"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.dsp.2015.08.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005426717"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.eswa.2015.07.030",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008669523"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40565-013-0032-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009198916",
"https://doi.org/10.1007/s40565-013-0032-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40565-013-0032-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009198916",
"https://doi.org/10.1007/s40565-013-0032-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ins.2014.02.077",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012731247"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3390/en8087752",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014365056"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.fss.2015.03.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014450316"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.energy.2014.06.104",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015131502"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40565-014-0094-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015624415",
"https://doi.org/10.1007/s40565-014-0094-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40565-014-0094-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015624415",
"https://doi.org/10.1007/s40565-014-0094-7"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.compag.2014.01.007",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016437262"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.energy.2015.03.054",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017954220"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40565-015-0103-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025343009",
"https://doi.org/10.1007/s40565-015-0103-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40565-015-0103-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025343009",
"https://doi.org/10.1007/s40565-015-0103-5"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.apenergy.2014.04.010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025736643"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/15325008.2014.896433",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026512013"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1155/2014/123675",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031396318"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40565-014-0059-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031550721",
"https://doi.org/10.1007/s40565-014-0059-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40565-014-0059-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031550721",
"https://doi.org/10.1007/s40565-014-0059-x"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cie.2013.03.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034265488"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.enbuild.2014.02.062",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034908400"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.apm.2013.05.035",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040088207"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.molstruc.2013.12.079",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040454283"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.omega.2014.05.007",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041306021"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.fss.2013.07.010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046933966"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ijepes.2014.05.073",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047824509"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.eswa.2014.06.006",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051012369"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tpwrs.2006.874539",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061777076"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tpwrs.2013.2293626",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061779333"
],
"type": "CreativeWork"
}
],
"datePublished": "2018-07",
"datePublishedReg": "2018-07-01",
"description": "Smart electricity utilization (SEU) is one of the most important components in a smart grid. It is crucial to evaluate efficiency, safety, and demand response capability of electricity users to achieve the smart use of electricity. The analytic hierarchy process (AHP) uses subjective criteria to determine index weights in multi-criteria decision-making problems, while the entropy method provides objectivity in determining index weights. Taking into account the uncertainty of expert scoring and user data, a hybrid interval analytic hierarchy process (IAHP) and interval entropy (IE) method is proposed for electricity user evaluation (EUE). Specifically, in the proposed method, electricity users are evaluated in terms of energy efficiency, safety monitoring, and demand response. The weights of EUE indices are calculated under uncertainty. The proposed approach derives subjective weights of EUE indices by the IAHP with expert scoring as input data, and determines objective weights of EUE indices by the IE method with user data as inputs. In order to obtain the optimal combined index weights, the two weights are normalized by a selected weight factor. Numerical case studies illustrate the effectiveness and advantages of the proposed approach, which combines subjective and objective information to derive the optimal combined index weights.",
"genre": "research_article",
"id": "sg:pub.10.1007/s40565-017-0355-3",
"inLanguage": [
"en"
],
"isAccessibleForFree": true,
"isPartOf": [
{
"id": "sg:journal.1135860",
"issn": [
"2196-5625",
"2196-5420"
],
"name": "Journal of Modern Power Systems and Clean Energy",
"type": "Periodical"
},
{
"issueNumber": "4",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "6"
}
],
"name": "Hybrid interval AHP-entropy method for electricity user evaluation in smart electricity utilization",
"pagination": "701-711",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"2287cdbaea2c16925fde1b2b9a72503674751f2e26e6676d44291e6192772b57"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s40565-017-0355-3"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1100075323"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s40565-017-0355-3",
"https://app.dimensions.ai/details/publication/pub.1100075323"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T02:30",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8700_00000603.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007/s40565-017-0355-3"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s40565-017-0355-3'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s40565-017-0355-3'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40565-017-0355-3'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40565-017-0355-3'
This table displays all metadata directly associated to this object as RDF triples.
171 TRIPLES
21 PREDICATES
54 URIs
19 LITERALS
7 BLANK NODES