Forecasting Uganda’s Net Electricity Consumption Using a Hybrid PSO-ABC Algorithm View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Abdal Kasule, Kürşat Ayan

ABSTRACT

Long-term electricity consumption forecasting is used by decision makers to make decisions regarding system expansion planning. Over the past decade, research on electricity consumption forecasting has reported results as point forecasts. Specifically for long-term forecasting, point forecasts are of little interest because it is hard to use them to assess the financial risk associated with system expansion versus demand variability and forecasting uncertainty. In this study, firstly we use power and quadratic forms to model Uganda’s net electricity consumption using population, gross domestic product, number of subscribers and average electricity price as variables in the forecasting models. We optimize the parameters of the forecasting models using a hybrid algorithm based on particle swarm optimization and artificial bee colony algorithms. Secondly we model the forecast residuals using simple linear regression to obtain 90% significance level lower and upper error bounds. The lower and upper error bounds were used to construct predication intervals for Uganda’s net electricity consumption up to year 2040. We accessed the accuracy and performance of the models using MAPE and R2. We obtained 1.4387 and 1.1741% for MAPE and 0.9920 and 0.9948 for R2, respectively, for power and quadratic models. According to our results, in year 2040 Uganda’s electricity consumption will be between [35,471.5, 36,317.6] GWh for the power model indicating an annual average increase of 10.1–11.3%. For the quadratic model consumption is expected to be between [47,443.1, 48,289.4] GWh which indicates an average annual increase of 11.2–12.4%. More... »

PAGES

3021-3031

References to SciGraph publications

Journal

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13369-018-3383-z

DOI

http://dx.doi.org/10.1007/s13369-018-3383-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104894188


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/1403", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Econometrics", 
        "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": "Sakarya University", 
          "id": "https://www.grid.ac/institutes/grid.49746.38", 
          "name": [
            "Department of Computer Engineering, Faculty of Computer and Information Science, Sakarya University, Sakarya, Turkey"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kasule", 
        "givenName": "Abdal", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Sakarya University", 
          "id": "https://www.grid.ac/institutes/grid.49746.38", 
          "name": [
            "Department of Computer Engineering, Faculty of Computer and Information Science, Sakarya University, Sakarya, Turkey"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ayan", 
        "givenName": "K\u00fcr\u015fat", 
        "id": "sg:person.07445276077.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07445276077.53"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.enpol.2009.12.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002550092"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.asoc.2007.05.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006312367"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.apenergy.2009.04.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009531780"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enpol.2007.01.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010653461"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enpol.2006.12.025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018397668"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ins.2010.07.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018674829"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10462-011-9276-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018811096", 
          "https://doi.org/10.1007/s10462-011-9276-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enpol.2009.09.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019398108"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sbspro.2012.09.1144", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019789094"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijforecast.2015.11.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022553156"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.energy.2010.07.043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025260063"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enconman.2009.10.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029034591"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enconman.2009.06.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031254679"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.rser.2004.09.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039131667"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enpol.2011.11.090", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039648604"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2015.07.043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040004146"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.knosys.2012.06.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040313588"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.apenergy.2010.12.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040424608"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.asoc.2008.03.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040659147"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.apenergy.2010.07.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044843352"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.asoc.2011.01.037", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045228292"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enpol.2014.11.035", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047476679"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.energy.2013.12.031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048858742"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.amc.2009.03.090", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050846875"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tevc.2007.896686", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061604803"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpwrs.2009.2036017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061777946"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5267/j.msl.2014.10.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072735310"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.9790/4200-0333843", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074179923"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.enpol.2017.03.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084820475"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.asoc.2017.04.025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085108734"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cec.2001.934374", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094578610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-981-10-6890-4_26", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103790768", 
          "https://doi.org/10.1007/978-981-10-6890-4_26"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-04", 
    "datePublishedReg": "2019-04-01", 
    "description": "Long-term electricity consumption forecasting is used by decision makers to make decisions regarding system expansion planning. Over the past decade, research on electricity consumption forecasting has reported results as point forecasts. Specifically for long-term forecasting, point forecasts are of little interest because it is hard to use them to assess the financial risk associated with system expansion versus demand variability and forecasting uncertainty. In this study, firstly we use power and quadratic forms to model Uganda\u2019s net electricity consumption using population, gross domestic product, number of subscribers and average electricity price as variables in the forecasting models. We optimize the parameters of the forecasting models using a hybrid algorithm based on particle swarm optimization and artificial bee colony algorithms. Secondly we model the forecast residuals using simple linear regression to obtain 90% significance level lower and upper error bounds. The lower and upper error bounds were used to construct predication intervals for Uganda\u2019s net electricity consumption up to year 2040. We accessed the accuracy and performance of the models using MAPE and R2. We obtained 1.4387 and 1.1741% for MAPE and 0.9920 and 0.9948 for R2, respectively, for power and quadratic models. According to our results, in year 2040 Uganda\u2019s electricity consumption will be between [35,471.5, 36,317.6] GWh for the power model indicating an annual average increase of 10.1\u201311.3%. For the quadratic model consumption is expected to be between [47,443.1, 48,289.4] GWh which indicates an average annual increase of 11.2\u201312.4%.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s13369-018-3383-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136851", 
        "issn": [
          "2193-567X", 
          "2191-4281"
        ], 
        "name": "Arabian Journal for Science and Engineering", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "44"
      }
    ], 
    "name": "Forecasting Uganda\u2019s Net Electricity Consumption Using a Hybrid PSO-ABC Algorithm", 
    "pagination": "3021-3031", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "3cdb20d034c8f33a5d42968a4db2505c8f1a58b3fc59373c574e1f90d3c5baea"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s13369-018-3383-z"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1104894188"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s13369-018-3383-z", 
      "https://app.dimensions.ai/details/publication/pub.1104894188"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:40", 
    "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/v90/0000000363_0000000363/records_70049_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs13369-018-3383-z"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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/s13369-018-3383-z'

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/s13369-018-3383-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13369-018-3383-z'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13369-018-3383-z'


 

This table displays all metadata directly associated to this object as RDF triples.

165 TRIPLES      21 PREDICATES      59 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s13369-018-3383-z schema:about anzsrc-for:14
2 anzsrc-for:1403
3 schema:author Nb2db2f028470487b8d5965846153a9f8
4 schema:citation sg:pub.10.1007/978-981-10-6890-4_26
5 sg:pub.10.1007/s10462-011-9276-0
6 https://doi.org/10.1016/j.amc.2009.03.090
7 https://doi.org/10.1016/j.apenergy.2009.04.024
8 https://doi.org/10.1016/j.apenergy.2010.07.021
9 https://doi.org/10.1016/j.apenergy.2010.12.005
10 https://doi.org/10.1016/j.asoc.2007.05.007
11 https://doi.org/10.1016/j.asoc.2008.03.001
12 https://doi.org/10.1016/j.asoc.2011.01.037
13 https://doi.org/10.1016/j.asoc.2017.04.025
14 https://doi.org/10.1016/j.enconman.2009.06.016
15 https://doi.org/10.1016/j.enconman.2009.10.013
16 https://doi.org/10.1016/j.energy.2010.07.043
17 https://doi.org/10.1016/j.energy.2013.12.031
18 https://doi.org/10.1016/j.enpol.2006.12.025
19 https://doi.org/10.1016/j.enpol.2007.01.028
20 https://doi.org/10.1016/j.enpol.2009.09.024
21 https://doi.org/10.1016/j.enpol.2009.12.016
22 https://doi.org/10.1016/j.enpol.2011.11.090
23 https://doi.org/10.1016/j.enpol.2014.11.035
24 https://doi.org/10.1016/j.enpol.2017.03.021
25 https://doi.org/10.1016/j.eswa.2015.07.043
26 https://doi.org/10.1016/j.ijforecast.2015.11.011
27 https://doi.org/10.1016/j.ins.2010.07.015
28 https://doi.org/10.1016/j.knosys.2012.06.009
29 https://doi.org/10.1016/j.rser.2004.09.004
30 https://doi.org/10.1016/j.sbspro.2012.09.1144
31 https://doi.org/10.1109/cec.2001.934374
32 https://doi.org/10.1109/tevc.2007.896686
33 https://doi.org/10.1109/tpwrs.2009.2036017
34 https://doi.org/10.5267/j.msl.2014.10.006
35 https://doi.org/10.9790/4200-0333843
36 schema:datePublished 2019-04
37 schema:datePublishedReg 2019-04-01
38 schema:description Long-term electricity consumption forecasting is used by decision makers to make decisions regarding system expansion planning. Over the past decade, research on electricity consumption forecasting has reported results as point forecasts. Specifically for long-term forecasting, point forecasts are of little interest because it is hard to use them to assess the financial risk associated with system expansion versus demand variability and forecasting uncertainty. In this study, firstly we use power and quadratic forms to model Uganda’s net electricity consumption using population, gross domestic product, number of subscribers and average electricity price as variables in the forecasting models. We optimize the parameters of the forecasting models using a hybrid algorithm based on particle swarm optimization and artificial bee colony algorithms. Secondly we model the forecast residuals using simple linear regression to obtain 90% significance level lower and upper error bounds. The lower and upper error bounds were used to construct predication intervals for Uganda’s net electricity consumption up to year 2040. We accessed the accuracy and performance of the models using MAPE and R2. We obtained 1.4387 and 1.1741% for MAPE and 0.9920 and 0.9948 for R2, respectively, for power and quadratic models. According to our results, in year 2040 Uganda’s electricity consumption will be between [35,471.5, 36,317.6] GWh for the power model indicating an annual average increase of 10.1–11.3%. For the quadratic model consumption is expected to be between [47,443.1, 48,289.4] GWh which indicates an average annual increase of 11.2–12.4%.
39 schema:genre research_article
40 schema:inLanguage en
41 schema:isAccessibleForFree false
42 schema:isPartOf N05e5a138ce0a44eda4f26a6669abe41e
43 N8e1951080d3e43f4807d9c14f7747e98
44 sg:journal.1136851
45 schema:name Forecasting Uganda’s Net Electricity Consumption Using a Hybrid PSO-ABC Algorithm
46 schema:pagination 3021-3031
47 schema:productId N002eeccc532c4d3c8529658e5032f4e2
48 N2d05351f33ba452b9e83f41f11d5c38e
49 Nf0fa082eeb0145f08c7bec21ecf4cd95
50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104894188
51 https://doi.org/10.1007/s13369-018-3383-z
52 schema:sdDatePublished 2019-04-11T12:40
53 schema:sdLicense https://scigraph.springernature.com/explorer/license/
54 schema:sdPublisher Nb9c1c6cb42734f76b377570ee1ebdaca
55 schema:url https://link.springer.com/10.1007%2Fs13369-018-3383-z
56 sgo:license sg:explorer/license/
57 sgo:sdDataset articles
58 rdf:type schema:ScholarlyArticle
59 N002eeccc532c4d3c8529658e5032f4e2 schema:name doi
60 schema:value 10.1007/s13369-018-3383-z
61 rdf:type schema:PropertyValue
62 N05e5a138ce0a44eda4f26a6669abe41e schema:volumeNumber 44
63 rdf:type schema:PublicationVolume
64 N2d05351f33ba452b9e83f41f11d5c38e schema:name dimensions_id
65 schema:value pub.1104894188
66 rdf:type schema:PropertyValue
67 N50338767b2a04351a6e36a3cb14d8a28 schema:affiliation https://www.grid.ac/institutes/grid.49746.38
68 schema:familyName Kasule
69 schema:givenName Abdal
70 rdf:type schema:Person
71 N8e1951080d3e43f4807d9c14f7747e98 schema:issueNumber 4
72 rdf:type schema:PublicationIssue
73 Na71832eb775c4d359f250fb1bd6dd3da rdf:first sg:person.07445276077.53
74 rdf:rest rdf:nil
75 Nb2db2f028470487b8d5965846153a9f8 rdf:first N50338767b2a04351a6e36a3cb14d8a28
76 rdf:rest Na71832eb775c4d359f250fb1bd6dd3da
77 Nb9c1c6cb42734f76b377570ee1ebdaca schema:name Springer Nature - SN SciGraph project
78 rdf:type schema:Organization
79 Nf0fa082eeb0145f08c7bec21ecf4cd95 schema:name readcube_id
80 schema:value 3cdb20d034c8f33a5d42968a4db2505c8f1a58b3fc59373c574e1f90d3c5baea
81 rdf:type schema:PropertyValue
82 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
83 schema:name Economics
84 rdf:type schema:DefinedTerm
85 anzsrc-for:1403 schema:inDefinedTermSet anzsrc-for:
86 schema:name Econometrics
87 rdf:type schema:DefinedTerm
88 sg:journal.1136851 schema:issn 2191-4281
89 2193-567X
90 schema:name Arabian Journal for Science and Engineering
91 rdf:type schema:Periodical
92 sg:person.07445276077.53 schema:affiliation https://www.grid.ac/institutes/grid.49746.38
93 schema:familyName Ayan
94 schema:givenName Kürşat
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07445276077.53
96 rdf:type schema:Person
97 sg:pub.10.1007/978-981-10-6890-4_26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103790768
98 https://doi.org/10.1007/978-981-10-6890-4_26
99 rdf:type schema:CreativeWork
100 sg:pub.10.1007/s10462-011-9276-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018811096
101 https://doi.org/10.1007/s10462-011-9276-0
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1016/j.amc.2009.03.090 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050846875
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1016/j.apenergy.2009.04.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009531780
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1016/j.apenergy.2010.07.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044843352
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1016/j.apenergy.2010.12.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040424608
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/j.asoc.2007.05.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006312367
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1016/j.asoc.2008.03.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040659147
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1016/j.asoc.2011.01.037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045228292
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1016/j.asoc.2017.04.025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085108734
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1016/j.enconman.2009.06.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031254679
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/j.enconman.2009.10.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029034591
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/j.energy.2010.07.043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025260063
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1016/j.energy.2013.12.031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048858742
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1016/j.enpol.2006.12.025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018397668
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1016/j.enpol.2007.01.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010653461
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/j.enpol.2009.09.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019398108
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/j.enpol.2009.12.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002550092
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1016/j.enpol.2011.11.090 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039648604
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1016/j.enpol.2014.11.035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047476679
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1016/j.enpol.2017.03.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084820475
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1016/j.eswa.2015.07.043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040004146
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1016/j.ijforecast.2015.11.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022553156
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1016/j.ins.2010.07.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018674829
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1016/j.knosys.2012.06.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040313588
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1016/j.rser.2004.09.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039131667
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1016/j.sbspro.2012.09.1144 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019789094
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1109/cec.2001.934374 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094578610
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1109/tevc.2007.896686 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061604803
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1109/tpwrs.2009.2036017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061777946
158 rdf:type schema:CreativeWork
159 https://doi.org/10.5267/j.msl.2014.10.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072735310
160 rdf:type schema:CreativeWork
161 https://doi.org/10.9790/4200-0333843 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074179923
162 rdf:type schema:CreativeWork
163 https://www.grid.ac/institutes/grid.49746.38 schema:alternateName Sakarya University
164 schema:name Department of Computer Engineering, Faculty of Computer and Information Science, Sakarya University, Sakarya, Turkey
165 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...