Short-term cloudiness forecasting for solar energy purposes in Greece, based on satellite-derived information View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

E. Nikitidou, A. Zagouras, V. Salamalikis, A. Kazantzidis

ABSTRACT

A novel method for the short-term (15–240 min) forecasting of cloudiness in Greece is presented by taking into account that this is the main atmospheric factor responsible for the spatial and temporal distribution of surface solar irradiance. Images from the Spinning Enhanced Visible and Infrared Imager onboard the Meteosat Second Generation satellite, for a 3-year time period and with high spatial and temporal resolution (0.05°, 15 min), were processed to retrieve the cloud clearness index (CCI) and used for the training and testing of an artificial neural network (ANN). The estimated and the measured values of CCI are in good agreement and emphasis is given to the spatial distribution of the seasonal errors. The ANN was trained according to pre-classified areas that present similar cloud characteristics and could provide estimations of surface solar irradiance in synergy with models that calculate surface irradiance under clear skies. More... »

PAGES

175-182

References to SciGraph publications

  • 1999-04. Effective Accuracy of Satellite-Derived Hourly Irradiances in THEORETICAL AND APPLIED CLIMATOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00703-017-0559-0

    DOI

    http://dx.doi.org/10.1007/s00703-017-0559-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092141300


    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/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Patras", 
              "id": "https://www.grid.ac/institutes/grid.11047.33", 
              "name": [
                "Laboratory of Atmospheric Physics, Physics Department, University of Patras, 26500, Patras, Greece"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Nikitidou", 
            "givenName": "E.", 
            "id": "sg:person.014641762704.27", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014641762704.27"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Patras", 
              "id": "https://www.grid.ac/institutes/grid.11047.33", 
              "name": [
                "Laboratory of Atmospheric Physics, Physics Department, University of Patras, 26500, Patras, Greece"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zagouras", 
            "givenName": "A.", 
            "id": "sg:person.014664214551.13", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014664214551.13"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Patras", 
              "id": "https://www.grid.ac/institutes/grid.11047.33", 
              "name": [
                "Laboratory of Atmospheric Physics, Physics Department, University of Patras, 26500, Patras, Greece"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Salamalikis", 
            "givenName": "V.", 
            "id": "sg:person.012441123655.65", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012441123655.65"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Patras", 
              "id": "https://www.grid.ac/institutes/grid.11047.33", 
              "name": [
                "Laboratory of Atmospheric Physics, Physics Department, University of Patras, 26500, Patras, Greece"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kazantzidis", 
            "givenName": "A.", 
            "id": "sg:person.01213136223.46", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213136223.46"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.enconman.2009.03.035", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001512603"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solener.2006.03.008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003068485"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.enconman.2013.07.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007510041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solener.2013.02.023", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012754805"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s1364-0321(01)00006-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014405502"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.enconman.2009.02.019", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015324310"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0038-092x(00)00038-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015656232"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.energy.2011.03.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016042559"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.energy.2015.07.103", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016219254"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0196-8904(03)00009-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018676591"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.energy.2009.05.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024934932"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.enconman.2015.10.033", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025735165"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solener.2011.01.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025906252"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.rse.2004.02.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028509719"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solener.2015.03.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029027501"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solener.2012.12.008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029338089"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s007040050084", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031014420", 
              "https://doi.org/10.1007/s007040050084"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3390/rs3020343", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033662461"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.energy.2013.09.008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036709603"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.energy.2015.08.043", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037801822"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solener.2004.04.017", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044584350"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1029/1999jd900302", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045949329"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solener.2004.09.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047342628"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.rser.2013.06.042", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047629692"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solener.2014.10.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048843887"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0038-092x(96)00162-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050080078"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0038-092x(02)00122-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050524409"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0038-092x(02)00122-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050524409"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solener.2013.08.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051049076"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solener.2015.10.041", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051362934"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-04", 
        "datePublishedReg": "2019-04-01", 
        "description": "A novel method for the short-term (15\u2013240 min) forecasting of cloudiness in Greece is presented by taking into account that this is the main atmospheric factor responsible for the spatial and temporal distribution of surface solar irradiance. Images from the Spinning Enhanced Visible and Infrared Imager onboard the Meteosat Second Generation satellite, for a 3-year time period and with high spatial and temporal resolution (0.05\u00b0, 15 min), were processed to retrieve the cloud clearness index (CCI) and used for the training and testing of an artificial neural network (ANN). The estimated and the measured values of CCI are in good agreement and emphasis is given to the spatial distribution of the seasonal errors. The ANN was trained according to pre-classified areas that present similar cloud characteristics and could provide estimations of surface solar irradiance in synergy with models that calculate surface irradiance under clear skies.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00703-017-0559-0", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1271293", 
            "issn": [
              "0177-7971", 
              "1436-5065"
            ], 
            "name": "Meteorology and Atmospheric Physics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "131"
          }
        ], 
        "name": "Short-term cloudiness forecasting for solar energy purposes in Greece, based on satellite-derived information", 
        "pagination": "175-182", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "188a79492cbacfd04b5d3ba5ad442c3fcf233073ea936f4e608253d31c8f42b9"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00703-017-0559-0"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1092141300"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00703-017-0559-0", 
          "https://app.dimensions.ai/details/publication/pub.1092141300"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T13:17", 
        "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/v90/0000000368_0000000368/records_78934_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs00703-017-0559-0"
      }
    ]
     

    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/s00703-017-0559-0'

    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/s00703-017-0559-0'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00703-017-0559-0'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00703-017-0559-0'


     

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

    170 TRIPLES      21 PREDICATES      56 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00703-017-0559-0 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N9733273f3b5c4c3db1ad80b94fd219be
    4 schema:citation sg:pub.10.1007/s007040050084
    5 https://doi.org/10.1016/j.enconman.2009.02.019
    6 https://doi.org/10.1016/j.enconman.2009.03.035
    7 https://doi.org/10.1016/j.enconman.2013.07.003
    8 https://doi.org/10.1016/j.enconman.2015.10.033
    9 https://doi.org/10.1016/j.energy.2009.05.009
    10 https://doi.org/10.1016/j.energy.2011.03.007
    11 https://doi.org/10.1016/j.energy.2013.09.008
    12 https://doi.org/10.1016/j.energy.2015.07.103
    13 https://doi.org/10.1016/j.energy.2015.08.043
    14 https://doi.org/10.1016/j.rse.2004.02.009
    15 https://doi.org/10.1016/j.rser.2013.06.042
    16 https://doi.org/10.1016/j.solener.2004.04.017
    17 https://doi.org/10.1016/j.solener.2004.09.007
    18 https://doi.org/10.1016/j.solener.2006.03.008
    19 https://doi.org/10.1016/j.solener.2011.01.007
    20 https://doi.org/10.1016/j.solener.2012.12.008
    21 https://doi.org/10.1016/j.solener.2013.02.023
    22 https://doi.org/10.1016/j.solener.2013.08.005
    23 https://doi.org/10.1016/j.solener.2014.10.002
    24 https://doi.org/10.1016/j.solener.2015.03.014
    25 https://doi.org/10.1016/j.solener.2015.10.041
    26 https://doi.org/10.1016/s0038-092x(00)00038-4
    27 https://doi.org/10.1016/s0038-092x(02)00122-6
    28 https://doi.org/10.1016/s0038-092x(96)00162-4
    29 https://doi.org/10.1016/s0196-8904(03)00009-8
    30 https://doi.org/10.1016/s1364-0321(01)00006-5
    31 https://doi.org/10.1029/1999jd900302
    32 https://doi.org/10.3390/rs3020343
    33 schema:datePublished 2019-04
    34 schema:datePublishedReg 2019-04-01
    35 schema:description A novel method for the short-term (15–240 min) forecasting of cloudiness in Greece is presented by taking into account that this is the main atmospheric factor responsible for the spatial and temporal distribution of surface solar irradiance. Images from the Spinning Enhanced Visible and Infrared Imager onboard the Meteosat Second Generation satellite, for a 3-year time period and with high spatial and temporal resolution (0.05°, 15 min), were processed to retrieve the cloud clearness index (CCI) and used for the training and testing of an artificial neural network (ANN). The estimated and the measured values of CCI are in good agreement and emphasis is given to the spatial distribution of the seasonal errors. The ANN was trained according to pre-classified areas that present similar cloud characteristics and could provide estimations of surface solar irradiance in synergy with models that calculate surface irradiance under clear skies.
    36 schema:genre research_article
    37 schema:inLanguage en
    38 schema:isAccessibleForFree false
    39 schema:isPartOf N08950d8cbfd74fdaaa41c1ac148e41d9
    40 N27faaff9377f4989b0048a65dfe1545c
    41 sg:journal.1271293
    42 schema:name Short-term cloudiness forecasting for solar energy purposes in Greece, based on satellite-derived information
    43 schema:pagination 175-182
    44 schema:productId N0d7920652bab4fb4977d3c755f0671e8
    45 N65a3a33699ee43279521c0093e3a544a
    46 N8f850085ef364fa1902fde701d114718
    47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092141300
    48 https://doi.org/10.1007/s00703-017-0559-0
    49 schema:sdDatePublished 2019-04-11T13:17
    50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    51 schema:sdPublisher N3a66536347844f9b879721c662de5018
    52 schema:url https://link.springer.com/10.1007%2Fs00703-017-0559-0
    53 sgo:license sg:explorer/license/
    54 sgo:sdDataset articles
    55 rdf:type schema:ScholarlyArticle
    56 N08950d8cbfd74fdaaa41c1ac148e41d9 schema:volumeNumber 131
    57 rdf:type schema:PublicationVolume
    58 N0d7920652bab4fb4977d3c755f0671e8 schema:name dimensions_id
    59 schema:value pub.1092141300
    60 rdf:type schema:PropertyValue
    61 N27faaff9377f4989b0048a65dfe1545c schema:issueNumber 2
    62 rdf:type schema:PublicationIssue
    63 N291e232a7a054d8b9767b8d9380c480d rdf:first sg:person.01213136223.46
    64 rdf:rest rdf:nil
    65 N3a66536347844f9b879721c662de5018 schema:name Springer Nature - SN SciGraph project
    66 rdf:type schema:Organization
    67 N6146aa3f097b47a490fb4363e8629186 rdf:first sg:person.014664214551.13
    68 rdf:rest Nbb42467205e047e89c58af2257a4ad5f
    69 N65a3a33699ee43279521c0093e3a544a schema:name readcube_id
    70 schema:value 188a79492cbacfd04b5d3ba5ad442c3fcf233073ea936f4e608253d31c8f42b9
    71 rdf:type schema:PropertyValue
    72 N8f850085ef364fa1902fde701d114718 schema:name doi
    73 schema:value 10.1007/s00703-017-0559-0
    74 rdf:type schema:PropertyValue
    75 N9733273f3b5c4c3db1ad80b94fd219be rdf:first sg:person.014641762704.27
    76 rdf:rest N6146aa3f097b47a490fb4363e8629186
    77 Nbb42467205e047e89c58af2257a4ad5f rdf:first sg:person.012441123655.65
    78 rdf:rest N291e232a7a054d8b9767b8d9380c480d
    79 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    80 schema:name Information and Computing Sciences
    81 rdf:type schema:DefinedTerm
    82 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    83 schema:name Artificial Intelligence and Image Processing
    84 rdf:type schema:DefinedTerm
    85 sg:journal.1271293 schema:issn 0177-7971
    86 1436-5065
    87 schema:name Meteorology and Atmospheric Physics
    88 rdf:type schema:Periodical
    89 sg:person.01213136223.46 schema:affiliation https://www.grid.ac/institutes/grid.11047.33
    90 schema:familyName Kazantzidis
    91 schema:givenName A.
    92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213136223.46
    93 rdf:type schema:Person
    94 sg:person.012441123655.65 schema:affiliation https://www.grid.ac/institutes/grid.11047.33
    95 schema:familyName Salamalikis
    96 schema:givenName V.
    97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012441123655.65
    98 rdf:type schema:Person
    99 sg:person.014641762704.27 schema:affiliation https://www.grid.ac/institutes/grid.11047.33
    100 schema:familyName Nikitidou
    101 schema:givenName E.
    102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014641762704.27
    103 rdf:type schema:Person
    104 sg:person.014664214551.13 schema:affiliation https://www.grid.ac/institutes/grid.11047.33
    105 schema:familyName Zagouras
    106 schema:givenName A.
    107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014664214551.13
    108 rdf:type schema:Person
    109 sg:pub.10.1007/s007040050084 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031014420
    110 https://doi.org/10.1007/s007040050084
    111 rdf:type schema:CreativeWork
    112 https://doi.org/10.1016/j.enconman.2009.02.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015324310
    113 rdf:type schema:CreativeWork
    114 https://doi.org/10.1016/j.enconman.2009.03.035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001512603
    115 rdf:type schema:CreativeWork
    116 https://doi.org/10.1016/j.enconman.2013.07.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007510041
    117 rdf:type schema:CreativeWork
    118 https://doi.org/10.1016/j.enconman.2015.10.033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025735165
    119 rdf:type schema:CreativeWork
    120 https://doi.org/10.1016/j.energy.2009.05.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024934932
    121 rdf:type schema:CreativeWork
    122 https://doi.org/10.1016/j.energy.2011.03.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016042559
    123 rdf:type schema:CreativeWork
    124 https://doi.org/10.1016/j.energy.2013.09.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036709603
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.1016/j.energy.2015.07.103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016219254
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1016/j.energy.2015.08.043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037801822
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.1016/j.rse.2004.02.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028509719
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1016/j.rser.2013.06.042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047629692
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.1016/j.solener.2004.04.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044584350
    135 rdf:type schema:CreativeWork
    136 https://doi.org/10.1016/j.solener.2004.09.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047342628
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.1016/j.solener.2006.03.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003068485
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1016/j.solener.2011.01.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025906252
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1016/j.solener.2012.12.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029338089
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1016/j.solener.2013.02.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012754805
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1016/j.solener.2013.08.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051049076
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1016/j.solener.2014.10.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048843887
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1016/j.solener.2015.03.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029027501
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1016/j.solener.2015.10.041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051362934
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1016/s0038-092x(00)00038-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015656232
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1016/s0038-092x(02)00122-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050524409
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1016/s0038-092x(96)00162-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050080078
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1016/s0196-8904(03)00009-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018676591
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1016/s1364-0321(01)00006-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014405502
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1029/1999jd900302 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045949329
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.3390/rs3020343 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033662461
    167 rdf:type schema:CreativeWork
    168 https://www.grid.ac/institutes/grid.11047.33 schema:alternateName University of Patras
    169 schema:name Laboratory of Atmospheric Physics, Physics Department, University of Patras, 26500, Patras, Greece
    170 rdf:type schema:Organization
     




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


    ...