Prediction of daily maximum temperature using a support vector regression algorithm

Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, sinc...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Renewable energy Ročník 36; číslo 11; s. 3054 - 3060
Hlavní autoři:	Paniagua-Tineo, A., Salcedo-Sanz, S., Casanova-Mateo, C., Ortiz-García, E.G., Cony, M.A., Hernández-Martín, E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 01.11.2011 Elsevier
Témata:	air conditioning Algorithms Applied sciences atmospheric pressure Daily maximum temperature prediction electricity Energy Europe Exact sciences and technology heat Heating Mathematical analysis Neural networks prediction Regression Relative humidity Stations Support vector regression algorithms temperature Vectors (mathematics) Europe Support vector regression algorithms Daily maximum temperature prediction Neural networks
ISSN:	0960-1481, 1879-0682
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, since it has been proven that electricity demand depends much on weather conditions. This paper presents a novel methodology for daily maximum temperature prediction, based on a Support Vector Regression approach. The paper is focused on different measuring stations in Europe, from which different meteorological variables have been obtained, including temperature, precipitation, relative humidity and air pressure. Two more variables are also included, specifically synoptic situation of the day and monthly cycle. Using this pool of prediction variables, it is shown that the SVMr algorithm is able to give an accurate prediction of the maximum temperature 24 h later. In the paper SVMr technique applied is fully described, including some bounds on the machine hyper-parameters in order to speed up the SVMr training process. The performance of the SVMr has been compared to that of different neural networks in the literature: a Multi-layer perceptron and an Extreme Learning Machine. ► This paper presents a SVMr approach to daily temperature prediction. ► Different measuring stations in Europe, with different meteorological variables are considered. ► Comparisons with alternative neural methods have shown the good performance of the proposed approach.
AbstractList	Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, since it has been proven that electricity demand depends much on weather conditions. This paper presents a novel methodology for daily maximum temperature prediction, based on a Support Vector Regression approach. The paper is focused on different measuring stations in Europe, from which different meteorological variables have been obtained, including temperature, precipitation, relative humidity and air pressure. Two more variables are also included, specifically synoptic situation of the day and monthly cycle. Using this pool of prediction variables, it is shown that the SVMr algorithm is able to give an accurate prediction of the maximum temperature 24 h later. In the paper SVMr technique applied is fully described, including some bounds on the machine hyper-parameters in order to speed up the SVMr training process. The performance of the SVMr has been compared to that of different neural networks in the literature: a Multi-layer perceptron and an Extreme Learning Machine. Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, since it has been proven that electricity demand depends much on weather conditions. This paper presents a novel methodology for daily maximum temperature prediction, based on a Support Vector Regression approach. The paper is focused on different measuring stations in Europe, from which different meteorological variables have been obtained, including temperature, precipitation, relative humidity and air pressure. Two more variables are also included, specifically synoptic situation of the day and monthly cycle. Using this pool of prediction variables, it is shown that the SVMr algorithm is able to give an accurate prediction of the maximum temperature 24 h later. In the paper SVMr technique applied is fully described, including some bounds on the machine hyper-parameters in order to speed up the SVMr training process. The performance of the SVMr has been compared to that of different neural networks in the literature: a Multi-layer perceptron and an Extreme Learning Machine. This paper presents a SVMr approach to daily temperature prediction. Different measuring stations in Europe, with different meteorological variables are considered. Comparisons with alternative neural methods have shown the good performance of the proposed approach. Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, since it has been proven that electricity demand depends much on weather conditions. This paper presents a novel methodology for daily maximum temperature prediction, based on a Support Vector Regression approach. The paper is focused on different measuring stations in Europe, from which different meteorological variables have been obtained, including temperature, precipitation, relative humidity and air pressure. Two more variables are also included, specifically synoptic situation of the day and monthly cycle. Using this pool of prediction variables, it is shown that the SVMr algorithm is able to give an accurate prediction of the maximum temperature 24 h later. In the paper SVMr technique applied is fully described, including some bounds on the machine hyper-parameters in order to speed up the SVMr training process. The performance of the SVMr has been compared to that of different neural networks in the literature: a Multi-layer perceptron and an Extreme Learning Machine. Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, since it has been proven that electricity demand depends much on weather conditions. This paper presents a novel methodology for daily maximum temperature prediction, based on a Support Vector Regression approach. The paper is focused on different measuring stations in Europe, from which different meteorological variables have been obtained, including temperature, precipitation, relative humidity and air pressure. Two more variables are also included, specifically synoptic situation of the day and monthly cycle. Using this pool of prediction variables, it is shown that the SVMr algorithm is able to give an accurate prediction of the maximum temperature 24 h later. In the paper SVMr technique applied is fully described, including some bounds on the machine hyper-parameters in order to speed up the SVMr training process. The performance of the SVMr has been compared to that of different neural networks in the literature: a Multi-layer perceptron and an Extreme Learning Machine. ► This paper presents a SVMr approach to daily temperature prediction. ► Different measuring stations in Europe, with different meteorological variables are considered. ► Comparisons with alternative neural methods have shown the good performance of the proposed approach.
Author	Ortiz-García, E.G. Cony, M.A. Hernández-Martín, E. Paniagua-Tineo, A. Casanova-Mateo, C. Salcedo-Sanz, S.
Author_xml	– sequence: 1 givenname: A. surname: Paniagua-Tineo fullname: Paniagua-Tineo, A. organization: Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain – sequence: 2 givenname: S. surname: Salcedo-Sanz fullname: Salcedo-Sanz, S. email: sancho.salcedo@uah.es organization: Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain – sequence: 3 givenname: C. surname: Casanova-Mateo fullname: Casanova-Mateo, C. organization: Department of Physics of the Earth, Astronomy and Astrophysics II, Universidad Complutense de Madrid, Spain – sequence: 4 givenname: E.G. surname: Ortiz-García fullname: Ortiz-García, E.G. organization: Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain – sequence: 5 givenname: M.A. surname: Cony fullname: Cony, M.A. organization: Department of Renewable Energy, Center for Energy, Environmental and Technologic Research (CIEMAT), Spain – sequence: 6 givenname: E. surname: Hernández-Martín fullname: Hernández-Martín, E. organization: Department of Physics of the Earth, Astronomy and Astrophysics II, Universidad Complutense de Madrid, Spain
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24272923$$DView record in Pascal Francis
BookMark	eNqFkkFrFTEQx4NU8LX6DQRzEb3sM8lmk6wHQYpWoaBQew7T7Owzj93NmmSL_fZm2eKhh5YM5PL7T2b45ZScTGFCQl5ztueMqw_HfcSpnL1gnO9ZXYo9IztudFsxZcQJ2bFWsYpLw1-Q05SOjPHGaLkjVz8jdt5lHyYaetqBH-7oCH_9uIw04zhjhLxEpEvy04ECTcs8h5jpLbocIo14iJjSGofhEKLPv8eX5HkPQ8JX9_cZuf765df5t-ryx8X388-XlZONyVV342rlmOobANMKIaBjzjllsGdStr1snOG17F3HbzotVI3SIAPeIWLbQlOfkXdb3zmGPwumbEefHA4DTBiWZMuC3CitV_L9oyTXWvOGKyOfRlVpybRga9e39ygkB0MfYXI-2Tn6EeKdFVJo0Yq6cHLjXAwpRez_I5zZVaA92k2gXQVaVpdiJfbxQcz5DKupHIump8JvtnAPwcIhlrmurwrQFPNlTaML8WkjsCi69Rhtch4nV75DLG5tF_zjT_wDp47FQA
CitedBy_id	crossref_primary_10_1371_journal_pone_0304754 crossref_primary_10_3390_electronics12071535 crossref_primary_10_1016_j_engappai_2012_10_018 crossref_primary_10_1088_2634_4505_acef57 crossref_primary_10_1007_s40808_025_02457_1 crossref_primary_10_1016_j_renene_2015_07_004 crossref_primary_10_1007_s11227_024_06909_w crossref_primary_10_1007_s10661_023_11143_7 crossref_primary_10_1016_j_compag_2017_11_038 crossref_primary_10_3390_en13164215 crossref_primary_10_1007_s12517_021_08484_3 crossref_primary_10_1016_j_asoc_2023_110118 crossref_primary_10_3390_en10050722 crossref_primary_10_3390_s25175295 crossref_primary_10_1016_j_enconman_2016_08_020 crossref_primary_10_1007_s12040_024_02501_1 crossref_primary_10_1016_j_egyr_2021_04_010 crossref_primary_10_1016_j_spasta_2015_05_008 crossref_primary_10_1007_s11600_022_00879_4 crossref_primary_10_1016_j_rser_2017_09_078 crossref_primary_10_1007_s00477_020_01898_7 crossref_primary_10_1080_10298436_2024_2381658 crossref_primary_10_1007_s10462_025_11247_1 crossref_primary_10_1016_j_atmosres_2014_01_012 crossref_primary_10_1007_s12145_022_00792_w crossref_primary_10_1016_j_atmosres_2023_106608 crossref_primary_10_1007_s00704_015_1480_4 crossref_primary_10_1016_j_applthermaleng_2012_09_030 crossref_primary_10_3390_math11051213 crossref_primary_10_1007_s00704_022_04103_7 crossref_primary_10_1016_j_cie_2023_109785 crossref_primary_10_1016_j_gloplacha_2015_03_006 crossref_primary_10_1007_s00343_020_0062_1 crossref_primary_10_1016_j_compag_2014_09_007 crossref_primary_10_1007_s12040_023_02091_4 crossref_primary_10_1016_j_renene_2012_01_084 crossref_primary_10_1016_j_aquaculture_2023_739284 crossref_primary_10_1002_joc_7988 crossref_primary_10_3390_forecast6030041 crossref_primary_10_1007_s41870_023_01185_y crossref_primary_10_1016_j_engappai_2014_05_016 crossref_primary_10_1016_j_renene_2014_02_041 crossref_primary_10_1007_s00704_023_04571_5 crossref_primary_10_1007_s12040_014_0434_z crossref_primary_10_1016_j_jobe_2020_101996 crossref_primary_10_1109_TVT_2024_3361160 crossref_primary_10_1016_j_jksuci_2017_12_004 crossref_primary_10_1016_j_eneco_2022_106291 crossref_primary_10_1080_0954898X_2020_1759833 crossref_primary_10_1016_j_rser_2013_03_004 crossref_primary_10_1016_j_renene_2014_06_010 crossref_primary_10_1002_joc_4249 crossref_primary_10_1007_s12273_021_0811_x crossref_primary_10_1080_10789669_2013_789371 crossref_primary_10_46632_cset_3_2_24 crossref_primary_10_1016_j_jhydrol_2018_11_069 crossref_primary_10_1016_j_agrformet_2024_109991 crossref_primary_10_1109_ACCESS_2022_3142174 crossref_primary_10_1016_j_compag_2016_01_019
Cites_doi	10.1016/0960-1481(94)00097-P 10.1016/S0960-1481(01)00082-9 10.1016/j.jmarsys.2008.11.026 10.1177/0309133309346647 10.5194/cp-5-259-2009 10.1016/S0140-9883(01)00082-2 10.1016/j.neucom.2009.07.009 10.1016/j.gloplacha.2004.06.005 10.1002/met.83 10.1007/s00704-005-0164-x 10.1002/joc.773 10.1002/(SICI)1099-131X(199603)15:2<97::AID-FOR608>3.0.CO;2-L 10.1016/j.compag.2009.04.003 10.1016/j.envsoft.2005.07.010 10.1016/j.renene.2003.11.009 10.1016/j.renene.2008.07.007 10.5194/nhess-9-1161-2009 10.1016/j.enbuild.2005.09.007 10.1007/s00704-009-0153-6 10.1002/joc.906 10.1016/S0196-8904(96)00117-3 10.1016/j.renene.2010.06.024 10.1127/0941-2948/2008/0314 10.1002/joc.3370060607 10.1109/72.329697 10.1115/1.1748466 10.1016/j.neucom.2005.12.126 10.1016/j.energy.2005.02.016 10.1016/j.engappai.2004.04.002 10.1007/s00704-008-0097-2 10.1017/S1350482704001318 10.1016/S0960-1481(98)00010-X 10.2134/agronj2009.0085 10.1002/joc.3370140606
ContentType	Journal Article
Copyright	2011 Elsevier Ltd 2014 INIST-CNRS
Copyright_xml	– notice: 2011 Elsevier Ltd – notice: 2014 INIST-CNRS
DBID	FBQ AAYXX CITATION IQODW 7S9 L.6 7SU 7TB 8FD C1K FR3 7ST 7U6 SOI
DOI	10.1016/j.renene.2011.03.030
DatabaseName	AGRIS CrossRef Pascal-Francis AGRICOLA AGRICOLA - Academic Environmental Engineering Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database Environment Abstracts Sustainability Science Abstracts Environment Abstracts
DatabaseTitle	CrossRef AGRICOLA AGRICOLA - Academic Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts Environmental Engineering Abstracts Environmental Sciences and Pollution Management Environment Abstracts Sustainability Science Abstracts
DatabaseTitleList	Engineering Research Database AGRICOLA Environment Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Applied Sciences
EISSN	1879-0682
EndPage	3060
ExternalDocumentID	24272923 10_1016_j_renene_2011_03_030 US201500168487 S0960148111001443
GeographicLocations	Europe
GeographicLocations_xml	– name: Europe
GroupedDBID	--K --M .~1 0R~ 123 1B1 1RT 1~. 1~5 29P 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARJD AAXUO ABFNM ABMAC ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHIDL AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BELTK BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HMC HVGLF HZ~ IHE J1W JARJE JJJVA K-O KOM LY6 LY9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAC SDF SDG SDP SEN SES SET SEW SPC SPCBC SSR SST SSZ T5K TN5 WUQ ZCA ~02 ~G- ABPIF ABPTK FBQ 9DU AAHBH AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEGFY AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD AFXIZ AGCQF AGRNS BNPGV IQODW SSH 7S9 L.6 7SU 7TB 8FD C1K FR3 7ST 7U6 SOI
ID	FETCH-LOGICAL-c458t-dbc36c06f5aa89222ad0ccc68ef0449f45c8134fcd1bd7263e48e0a1deee99a53
ISICitedReferencesCount	144
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000292441400036&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0960-1481
IngestDate	Tue Oct 07 09:45:20 EDT 2025 Tue Oct 07 09:37:21 EDT 2025 Thu Oct 02 05:11:52 EDT 2025 Mon Jul 21 09:16:16 EDT 2025 Tue Nov 18 21:51:35 EST 2025 Sat Nov 29 06:21:03 EST 2025 Wed Dec 27 19:00:57 EST 2023 Fri Feb 23 02:26:23 EST 2024
IsPeerReviewed	true
IsScholarly	true
Issue	11
Keywords	Support vector regression algorithms Daily maximum temperature prediction Neural networks
Language	English
License	https://www.elsevier.com/tdm/userlicense/1.0 CC BY 4.0
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c458t-dbc36c06f5aa89222ad0ccc68ef0449f45c8134fcd1bd7263e48e0a1deee99a53
Notes	http://dx.doi.org/10.1016/j.renene.2011.03.030 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
PQID	1686707205
PQPubID	23462
PageCount	7
ParticipantIDs	proquest_miscellaneous_874186775 proquest_miscellaneous_1777151684 proquest_miscellaneous_1686707205 pascalfrancis_primary_24272923 crossref_primary_10_1016_j_renene_2011_03_030 crossref_citationtrail_10_1016_j_renene_2011_03_030 fao_agris_US201500168487 elsevier_sciencedirect_doi_10_1016_j_renene_2011_03_030
PublicationCentury	2000
PublicationDate	2011-11-01
PublicationDateYYYYMMDD	2011-11-01
PublicationDate_xml	– month: 11 year: 2011 text: 2011-11-01 day: 01
PublicationDecade	2010
PublicationPlace	Oxford
PublicationPlace_xml	– name: Oxford
PublicationTitle	Renewable energy
PublicationYear	2011
Publisher	Elsevier Ltd Elsevier
Publisher_xml	– name: Elsevier Ltd – name: Elsevier
References	Prieto, García-Herrera, Daz, Hernández, del Teso (bib26) 2004; 44 Tasaduqq, Rehman Bubshait (bib11) 2002; 25 Pardo, Meneu, Valor (bib7) 2002; 24 Ododo (bib10) 1997; 38 Cahynová, Huth (bib32) 2009; 96 Kysely, Huth (bib36) 2006; 85 Huld, Suri, Dunlop, Micale (bib16) 2006; 21 Huang, Zhu, Siew (bib40) 2006; 70 Parishwad, Bhardwaj, Nema (bib3) 1998; 13 Begert, Zenklusen, Häberli, Appenzeller, Klok (bib23) 2008; 17 Abdel-Aal (bib1) 2004; 17 Chandler, Whitlock, Stackhouse (bib31) 2004; 126 Dombayc, Gölcü (bib4) 2009; 34 Bai, Chen, Dobermann, Yang, Cassman, Zhang (bib30) 2009; 102 Al Zayer, Al Ibrahim (bib6) 1996; 15 Smola, Schlkopf (bib17) 1998 Smith, Hoogenboom, McClendon (bib13) 2009; 68 Klein-Tank, Wijngaard, Können, Böhm, Demarée, Gocheva (bib22) 2002; 22 Ododo, Sulaiman, Aidan, Yuguda, Ogbu (bib9) 1995; 6 Chen, Liu, Wu, Xie (bib20) 2011; 36 Hurrell, Deser (bib35) 2009; 78 Peterson, Easterling (bib27) 1994; 14 Hagan, Menhaj (bib39) 1994; 5 Mohandes, Halawani, Rehman, Hussain (bib19) 2004; 29 Jones, Lister (bib33) 2009; 5 Ruano, Crispim, Conceiao, Lúcio (bib2) 2006; 38 . Mirasgedis, Sarafidis, Georgopoulou, Lalas, Moschovits, Karagiannis (bib8) 2006; 31 Smith, Hoogenboom, McClendon (bib12) 2007; 3 Kidd, Levizzani, Bauer (bib28) 2009; 33 Planchon Quénol, Dupont, Corgne (bib37) 2009; 9 Camia, Bovio, Aguando, Stach (bib5) 1999 Smola, Murata, Scholkopf, Muller (bib18) 1998 Ortiz-García, Salcedo-Sanz, Pérez-Bellido, Portilla-Figueras (bib21) 2009; 72 Alexandersson (bib25) 1986; 6 Van den Besselaar, Klein-Tank, Van der Schrier (bib34) 2010; 99 Wijngaard, Klein-Tank, Können (bib24) 2003; 23 Ustaoglu, Cigizoglu, Karaca (bib14) 2008; 15 Boi (bib15) 2004; 11 Bishop (bib38) 1995 Kidd (10.1016/j.renene.2011.03.030_bib28) 2009; 33 Ruano (10.1016/j.renene.2011.03.030_bib2) 2006; 38 Huld (10.1016/j.renene.2011.03.030_bib16) 2006; 21 Peterson (10.1016/j.renene.2011.03.030_bib27) 1994; 14 Pardo (10.1016/j.renene.2011.03.030_bib7) 2002; 24 Cahynová (10.1016/j.renene.2011.03.030_bib32) 2009; 96 10.1016/j.renene.2011.03.030_bib29 Smith (10.1016/j.renene.2011.03.030_bib12) 2007; 3 Alexandersson (10.1016/j.renene.2011.03.030_bib25) 1986; 6 Kysely (10.1016/j.renene.2011.03.030_bib36) 2006; 85 Tasaduqq (10.1016/j.renene.2011.03.030_bib11) 2002; 25 Smola (10.1016/j.renene.2011.03.030_bib18) 1998 Bishop (10.1016/j.renene.2011.03.030_bib38) 1995 Bai (10.1016/j.renene.2011.03.030_bib30) 2009; 102 Hagan (10.1016/j.renene.2011.03.030_bib39) 1994; 5 Jones (10.1016/j.renene.2011.03.030_bib33) 2009; 5 Boi (10.1016/j.renene.2011.03.030_bib15) 2004; 11 Ortiz-García (10.1016/j.renene.2011.03.030_bib21) 2009; 72 Wijngaard (10.1016/j.renene.2011.03.030_bib24) 2003; 23 Ododo (10.1016/j.renene.2011.03.030_bib9) 1995; 6 Hurrell (10.1016/j.renene.2011.03.030_bib35) 2009; 78 Dombayc (10.1016/j.renene.2011.03.030_bib4) 2009; 34 Mirasgedis (10.1016/j.renene.2011.03.030_bib8) 2006; 31 Mohandes (10.1016/j.renene.2011.03.030_bib19) 2004; 29 Chandler (10.1016/j.renene.2011.03.030_bib31) 2004; 126 Al Zayer (10.1016/j.renene.2011.03.030_bib6) 1996; 15 Ustaoglu (10.1016/j.renene.2011.03.030_bib14) 2008; 15 Planchon Quénol (10.1016/j.renene.2011.03.030_bib37) 2009; 9 Prieto (10.1016/j.renene.2011.03.030_bib26) 2004; 44 Huang (10.1016/j.renene.2011.03.030_bib40) 2006; 70 Smith (10.1016/j.renene.2011.03.030_bib13) 2009; 68 Smola (10.1016/j.renene.2011.03.030_bib17) 1998 Begert (10.1016/j.renene.2011.03.030_bib23) 2008; 17 Abdel-Aal (10.1016/j.renene.2011.03.030_bib1) 2004; 17 Camia (10.1016/j.renene.2011.03.030_bib5) 1999 Chen (10.1016/j.renene.2011.03.030_bib20) 2011; 36 Van den Besselaar (10.1016/j.renene.2011.03.030_bib34) 2010; 99 Ododo (10.1016/j.renene.2011.03.030_bib10) 1997; 38 Parishwad (10.1016/j.renene.2011.03.030_bib3) 1998; 13 Klein-Tank (10.1016/j.renene.2011.03.030_bib22) 2002; 22
References_xml	– volume: 24 start-page: 55 year: 2002 end-page: 70 ident: bib7 article-title: Temperature and seasonality influences on Spanish electricity load publication-title: Energy Economics – volume: 78 start-page: 28 year: 2009 end-page: 41 ident: bib35 article-title: North Atlantic climate variability: the role of the north Atlantic Oscillation publication-title: Journal of Marine Systems – volume: 85 start-page: 19 year: 2006 end-page: 36 ident: bib36 article-title: Changes in atmospheric circulation over Europe detected by objective and subjective methods publication-title: Theoretical and Applied Climatology – volume: 23 start-page: 679 year: 2003 end-page: 692 ident: bib24 article-title: Homogeneity of 20th century European daily temperature and precipitation series publication-title: International Journal of Climatology – volume: 34 start-page: 1158 year: 2009 end-page: 1161 ident: bib4 article-title: Daily means ambient temperature prediction using artificial neural networks method: a case study of Turkey publication-title: Renewable Energy – year: 1995 ident: bib38 article-title: Neural networks for pattern recognition – volume: 6 start-page: 751 year: 1995 end-page: 763 ident: bib9 article-title: The importance of maximum air temperature in the parameterisation of solar radiation in Nigeria publication-title: Renewable Energy – volume: 22 start-page: 1441 year: 2002 end-page: 1453 ident: bib22 article-title: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment publication-title: International Journal of Climatology – volume: 15 start-page: 97 year: 1996 end-page: 106 ident: bib6 article-title: Modelling the impact of temperature on electricity consumption in the eastern province of Saudi Arabia publication-title: Journal of Forecasting – volume: 5 start-page: 259 year: 2009 end-page: 267 ident: bib33 article-title: The influence of the circulation on surface temperature and precipitation patterns over Europe publication-title: Climate of the Past – volume: 5 start-page: 989 year: 1994 end-page: 993 ident: bib39 article-title: Training feed forward network with the Marquardt algorithm publication-title: IEEE Transactions on Neural Networks – volume: 17 start-page: 663 year: 2008 end-page: 672 ident: bib23 article-title: An automated procedure to detect discontinuities; performance assessment and application to a large European climate data set publication-title: Meteorologische Zeitschrift – volume: 17 start-page: 543 year: 2004 end-page: 556 ident: bib1 article-title: Hourly temperature forecasting using abductive networks publication-title: Engineering Applications of Artificial Intelligence – volume: 31 start-page: 208 year: 2006 end-page: 227 ident: bib8 article-title: Models for mid-term electricity demand forecasting incorporating weather influences publication-title: Energy – volume: 99 start-page: 431 year: 2010 end-page: 439 ident: bib34 article-title: Influence of circulation types on temperature extremes in Europe publication-title: Theoretical and Applied Climatology – year: 1998 ident: bib18 article-title: Asymptotically optimal choice of ϵ-loss for support vector machines publication-title: Proc. of the 8th international conference on artificial neural networks, perspectives in neural computing – volume: 29 start-page: 939 year: 2004 end-page: 947 ident: bib19 article-title: Support vector machines for wind speed prediction publication-title: Renewable Energy – volume: 70 start-page: 489 year: 2006 end-page: 501 ident: bib40 article-title: Extreme learning machine: theory and applications publication-title: Neurocomputing – volume: 3 start-page: 179 year: 2007 end-page: 186 ident: bib12 article-title: Improving air temperature prediction with artificial neural networks publication-title: International Journal of Computational Intelligence – volume: 33 start-page: 474 year: 2009 end-page: 489 ident: bib28 article-title: A review of satellite meteorology and climatology at the start of the twenty-first century publication-title: Progress in Physical Geography – volume: 102 start-page: 9 year: 2009 end-page: 16 ident: bib30 article-title: Evaluation of NASA satellite and model derived weather data for simulation of maize yield potential in China publication-title: Agronomy Journal – volume: 96 start-page: 57 year: 2009 end-page: 68 ident: bib32 article-title: Changes of atmospheric circulation in central Europe and their influence on climatic trends in the Czech Republic publication-title: Theoretical and Applied Climatology – volume: 38 start-page: 1807 year: 1997 end-page: 1814 ident: bib10 article-title: Prediction of solar radiation using only maximum temperature and relative humidity: South-east and north-east Nigeria publication-title: Energy Conversion and Management – volume: 36 start-page: 413 year: 2011 end-page: 420 ident: bib20 article-title: Estimation of monthly solar radiation from measured temperatures using support vector machines - A case study publication-title: Renewable Energy – volume: 21 start-page: 1650 year: 2006 end-page: 1661 ident: bib16 article-title: Estimating average daytime and daily temperature profiles within Europe publication-title: Environmental Modelling and Software – volume: 13 start-page: 363 year: 1998 end-page: 380 ident: bib3 article-title: Prediction of monthly-mean hourly relative humidity, ambient temperature and wind velocity for India publication-title: Renewable Energy – volume: 11 start-page: 245 year: 2004 end-page: 251 ident: bib15 article-title: A statistical method for forecasting extreme daily temperatures using ECMWF 2-m temperatures and ground station measurements publication-title: Meteorological Applications – volume: 68 start-page: 52 year: 2009 end-page: 61 ident: bib13 article-title: Artificial neural networks for automated year-round temperature prediction publication-title: Computers and Electronics in Agriculture – year: 1999 ident: bib5 article-title: “Meteorological fire danger indices and remote sensing,” Remote sensing of large wildfires in the European mediterranean basis – volume: 72 start-page: 3683 year: 2009 end-page: 3691 ident: bib21 article-title: Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions publication-title: Neurocomputing – volume: 9 start-page: 1161 year: 2009 end-page: 1173 ident: bib37 article-title: Application of the Hess-Brezowsky classification to the identification of weather patterns causing heavy winter rainfall in Brittany (France) publication-title: Natural Hazards and Earth System Sciences – year: 1998 ident: bib17 article-title: A tutorial on support vector regression publication-title: Statistics and Computing – volume: 44 start-page: 59 year: 2004 end-page: 71 ident: bib26 article-title: Minimum extreme temperatures over Peninsular Spain publication-title: Global and Planetary Change – volume: 14 start-page: 671 year: 1994 end-page: 679 ident: bib27 article-title: Creation of homogeneous composite climatological reference series publication-title: International Journal of Climatology – volume: 15 start-page: 431 year: 2008 end-page: 445 ident: bib14 article-title: Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods publication-title: Meteorological Applications – volume: 25 start-page: 545 year: 2002 end-page: 554 ident: bib11 article-title: Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia publication-title: Renewable Energy – volume: 6 start-page: 661 year: 1986 end-page: 675 ident: bib25 article-title: A homogeneity test applied to precipitation data publication-title: Journal of Climatology – volume: 38 start-page: 682 year: 2006 end-page: 694 ident: bib2 article-title: Prediction of building’s temperature using neural networks models publication-title: Energy and Buidings – volume: 126 start-page: 945 year: 2004 end-page: 950 ident: bib31 article-title: NASA climatological data for renewable energy assessment publication-title: Journal of Solar Energy Engineering-transaction of the ASME – reference: . – volume: 6 start-page: 751 issue: no. 7 year: 1995 ident: 10.1016/j.renene.2011.03.030_bib9 article-title: The importance of maximum air temperature in the parameterisation of solar radiation in Nigeria publication-title: Renewable Energy doi: 10.1016/0960-1481(94)00097-P – volume: 25 start-page: 545 year: 2002 ident: 10.1016/j.renene.2011.03.030_bib11 article-title: Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia publication-title: Renewable Energy doi: 10.1016/S0960-1481(01)00082-9 – year: 1998 ident: 10.1016/j.renene.2011.03.030_bib18 article-title: Asymptotically optimal choice of ϵ-loss for support vector machines – year: 1995 ident: 10.1016/j.renene.2011.03.030_bib38 – volume: 78 start-page: 28 issue: no. 1 year: 2009 ident: 10.1016/j.renene.2011.03.030_bib35 article-title: North Atlantic climate variability: the role of the north Atlantic Oscillation publication-title: Journal of Marine Systems doi: 10.1016/j.jmarsys.2008.11.026 – volume: 33 start-page: 474 issue: no. 4 year: 2009 ident: 10.1016/j.renene.2011.03.030_bib28 article-title: A review of satellite meteorology and climatology at the start of the twenty-first century publication-title: Progress in Physical Geography doi: 10.1177/0309133309346647 – volume: 5 start-page: 259 year: 2009 ident: 10.1016/j.renene.2011.03.030_bib33 article-title: The influence of the circulation on surface temperature and precipitation patterns over Europe publication-title: Climate of the Past doi: 10.5194/cp-5-259-2009 – volume: 24 start-page: 55 year: 2002 ident: 10.1016/j.renene.2011.03.030_bib7 article-title: Temperature and seasonality influences on Spanish electricity load publication-title: Energy Economics doi: 10.1016/S0140-9883(01)00082-2 – volume: 72 start-page: 3683 year: 2009 ident: 10.1016/j.renene.2011.03.030_bib21 article-title: Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions publication-title: Neurocomputing doi: 10.1016/j.neucom.2009.07.009 – volume: 44 start-page: 59 issue: no. 1–4 year: 2004 ident: 10.1016/j.renene.2011.03.030_bib26 article-title: Minimum extreme temperatures over Peninsular Spain publication-title: Global and Planetary Change doi: 10.1016/j.gloplacha.2004.06.005 – volume: 15 start-page: 431 year: 2008 ident: 10.1016/j.renene.2011.03.030_bib14 article-title: Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods publication-title: Meteorological Applications doi: 10.1002/met.83 – volume: 85 start-page: 19 year: 2006 ident: 10.1016/j.renene.2011.03.030_bib36 article-title: Changes in atmospheric circulation over Europe detected by objective and subjective methods publication-title: Theoretical and Applied Climatology doi: 10.1007/s00704-005-0164-x – volume: 22 start-page: 1441 issue: no. 12 year: 2002 ident: 10.1016/j.renene.2011.03.030_bib22 article-title: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment publication-title: International Journal of Climatology doi: 10.1002/joc.773 – volume: 15 start-page: 97 year: 1996 ident: 10.1016/j.renene.2011.03.030_bib6 article-title: Modelling the impact of temperature on electricity consumption in the eastern province of Saudi Arabia publication-title: Journal of Forecasting doi: 10.1002/(SICI)1099-131X(199603)15:2<97::AID-FOR608>3.0.CO;2-L – year: 1999 ident: 10.1016/j.renene.2011.03.030_bib5 – volume: 68 start-page: 52 year: 2009 ident: 10.1016/j.renene.2011.03.030_bib13 article-title: Artificial neural networks for automated year-round temperature prediction publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2009.04.003 – volume: 21 start-page: 1650 year: 2006 ident: 10.1016/j.renene.2011.03.030_bib16 article-title: Estimating average daytime and daily temperature profiles within Europe publication-title: Environmental Modelling and Software doi: 10.1016/j.envsoft.2005.07.010 – volume: 29 start-page: 939 issue: no. 6 year: 2004 ident: 10.1016/j.renene.2011.03.030_bib19 article-title: Support vector machines for wind speed prediction publication-title: Renewable Energy doi: 10.1016/j.renene.2003.11.009 – volume: 34 start-page: 1158 year: 2009 ident: 10.1016/j.renene.2011.03.030_bib4 article-title: Daily means ambient temperature prediction using artificial neural networks method: a case study of Turkey publication-title: Renewable Energy doi: 10.1016/j.renene.2008.07.007 – ident: 10.1016/j.renene.2011.03.030_bib29 – volume: 9 start-page: 1161 year: 2009 ident: 10.1016/j.renene.2011.03.030_bib37 article-title: Application of the Hess-Brezowsky classification to the identification of weather patterns causing heavy winter rainfall in Brittany (France) publication-title: Natural Hazards and Earth System Sciences doi: 10.5194/nhess-9-1161-2009 – volume: 38 start-page: 682 year: 2006 ident: 10.1016/j.renene.2011.03.030_bib2 article-title: Prediction of building’s temperature using neural networks models publication-title: Energy and Buidings doi: 10.1016/j.enbuild.2005.09.007 – volume: 99 start-page: 431 year: 2010 ident: 10.1016/j.renene.2011.03.030_bib34 article-title: Influence of circulation types on temperature extremes in Europe publication-title: Theoretical and Applied Climatology doi: 10.1007/s00704-009-0153-6 – volume: 23 start-page: 679 issue: no. 6 year: 2003 ident: 10.1016/j.renene.2011.03.030_bib24 article-title: Homogeneity of 20th century European daily temperature and precipitation series publication-title: International Journal of Climatology doi: 10.1002/joc.906 – volume: 38 start-page: 1807 issue: no. 18 year: 1997 ident: 10.1016/j.renene.2011.03.030_bib10 article-title: Prediction of solar radiation using only maximum temperature and relative humidity: South-east and north-east Nigeria publication-title: Energy Conversion and Management doi: 10.1016/S0196-8904(96)00117-3 – volume: 36 start-page: 413 issue: no. 1 year: 2011 ident: 10.1016/j.renene.2011.03.030_bib20 article-title: Estimation of monthly solar radiation from measured temperatures using support vector machines - A case study publication-title: Renewable Energy doi: 10.1016/j.renene.2010.06.024 – volume: 17 start-page: 663 issue: no. 5 year: 2008 ident: 10.1016/j.renene.2011.03.030_bib23 article-title: An automated procedure to detect discontinuities; performance assessment and application to a large European climate data set publication-title: Meteorologische Zeitschrift doi: 10.1127/0941-2948/2008/0314 – volume: 6 start-page: 661 year: 1986 ident: 10.1016/j.renene.2011.03.030_bib25 article-title: A homogeneity test applied to precipitation data publication-title: Journal of Climatology doi: 10.1002/joc.3370060607 – volume: 5 start-page: 989 issue: no. 6 year: 1994 ident: 10.1016/j.renene.2011.03.030_bib39 article-title: Training feed forward network with the Marquardt algorithm publication-title: IEEE Transactions on Neural Networks doi: 10.1109/72.329697 – volume: 126 start-page: 945 issue: no. 3 year: 2004 ident: 10.1016/j.renene.2011.03.030_bib31 article-title: NASA climatological data for renewable energy assessment publication-title: Journal of Solar Energy Engineering-transaction of the ASME doi: 10.1115/1.1748466 – volume: 70 start-page: 489 issue: no. 1–3 year: 2006 ident: 10.1016/j.renene.2011.03.030_bib40 article-title: Extreme learning machine: theory and applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – volume: 31 start-page: 208 year: 2006 ident: 10.1016/j.renene.2011.03.030_bib8 article-title: Models for mid-term electricity demand forecasting incorporating weather influences publication-title: Energy doi: 10.1016/j.energy.2005.02.016 – volume: 17 start-page: 543 year: 2004 ident: 10.1016/j.renene.2011.03.030_bib1 article-title: Hourly temperature forecasting using abductive networks publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2004.04.002 – volume: 3 start-page: 179 issue: no. 3 year: 2007 ident: 10.1016/j.renene.2011.03.030_bib12 article-title: Improving air temperature prediction with artificial neural networks publication-title: International Journal of Computational Intelligence – volume: 96 start-page: 57 issue: no. 1–2 year: 2009 ident: 10.1016/j.renene.2011.03.030_bib32 article-title: Changes of atmospheric circulation in central Europe and their influence on climatic trends in the Czech Republic publication-title: Theoretical and Applied Climatology doi: 10.1007/s00704-008-0097-2 – volume: 11 start-page: 245 year: 2004 ident: 10.1016/j.renene.2011.03.030_bib15 article-title: A statistical method for forecasting extreme daily temperatures using ECMWF 2-m temperatures and ground station measurements publication-title: Meteorological Applications doi: 10.1017/S1350482704001318 – volume: 13 start-page: 363 year: 1998 ident: 10.1016/j.renene.2011.03.030_bib3 article-title: Prediction of monthly-mean hourly relative humidity, ambient temperature and wind velocity for India publication-title: Renewable Energy doi: 10.1016/S0960-1481(98)00010-X – year: 1998 ident: 10.1016/j.renene.2011.03.030_bib17 article-title: A tutorial on support vector regression publication-title: Statistics and Computing – volume: 102 start-page: 9 year: 2009 ident: 10.1016/j.renene.2011.03.030_bib30 article-title: Evaluation of NASA satellite and model derived weather data for simulation of maize yield potential in China publication-title: Agronomy Journal doi: 10.2134/agronj2009.0085 – volume: 14 start-page: 671 year: 1994 ident: 10.1016/j.renene.2011.03.030_bib27 article-title: Creation of homogeneous composite climatological reference series publication-title: International Journal of Climatology doi: 10.1002/joc.3370140606
SSID	ssj0015874
Score	2.415268
Snippet	Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air...
SourceID	proquest pascalfrancis crossref fao elsevier
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	3054
SubjectTerms	air conditioning Algorithms Applied sciences atmospheric pressure Daily maximum temperature prediction electricity Energy Europe Exact sciences and technology heat Heating Mathematical analysis Neural networks prediction Regression Relative humidity Stations Support vector regression algorithms temperature Vectors (mathematics)
Title	Prediction of daily maximum temperature using a support vector regression algorithm
URI	https://dx.doi.org/10.1016/j.renene.2011.03.030 https://www.proquest.com/docview/1686707205 https://www.proquest.com/docview/1777151684 https://www.proquest.com/docview/874186775
Volume	36
WOSCitedRecordID	wos000292441400036&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-0682 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0015874 issn: 0960-1481 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLa6jQd44DKYVi6TkXirPDmJYzuPUSmjCNpp7dC0l8hN7NKpTafe6M_gJ3NybcoYhQekKIosO7fv8_E5xz7HCL3T2pPac0IyiIwiTHFGBpRpYhyquacMsyKabjYhOh15deWd12o_iliY1VjEsVyvvdv_CjWUAdhJ6Ow_wF3eFArgGkCHM8AO578C_nyWzL0UimCkEv_FRK1Hk-WkkSSiyrMoN5apl0A15svbRAdvrFL_fWOmh9nS2LihxsPpbLT4NqlqsBcgHL-n8VY6DRssZavfaftnlz7ptzut7paXtOd_brbed0nP71xveVubPhR1v_rki9_P2pQ-2-5Fv31NzvyLZttP5fVpvgtYVLpcrS0PRRE6U_U9ckrADrOqYjjLg1LQzaoIVRBJrDJAg5FDfyv8Mz_EzWmSCzTWeXpWBw66GeyKCf5fxsByZSJoLGBu2M4eOrCF68l9dOC3W1efypkpV2aZvYtPKMIx0zWDd598n7qzZ9Q0WYer5tAVTbaHyh11INVx-k_R49w4wX5GqmeopuND9CQ3VHA-DMwP0aNKFsvnqLdhHJ4anDIO54zDFcbhlHFY4ZxxOGMc3jAOl4x7gS4_tPrNjyTfq4OEzJULEg1Ch4eUG1cp6YHSqSIahiGX2lDGPMPcUFoOM2FkDSJhc0czqamyIg3SwlOuc4T242msjxFmNLSEY7hRRjBjczDIue0pKpXDXRB8deQUvzQI80T2yX4q46BYsXgTZEAECRABdeCgdUTKVrdZIpcd9UWBVpAro5mSGQDddrQ8BnADNYRROrjs2YlPEepKJkUdnWwhXr5JQbs6eltQIAA5n0zeqVhPl_MAbsAFFTZ1_1BHCAEaPDyqjvA9dWSSrooL4b7c9S6v0MNNb36N9hezpX6DHoSrxWg-O8n7xU9Dld16
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Prediction+of+daily+maximum+temperature+using+a+support+vector+regression+algorithm&rft.jtitle=Renewable+energy&rft.au=PANIAGUA-TINEO%2C+A&rft.au=SALCEDO-SANZ%2C+S&rft.au=CASANOVA-MATEO%2C+C&rft.au=ORTIZ-GARCIA%2C+E.+G&rft.date=2011-11-01&rft.pub=Elsevier&rft.issn=0960-1481&rft.volume=36&rft.issue=11&rft.spage=3054&rft.epage=3060&rft_id=info:doi/10.1016%2Fj.renene.2011.03.030&rft.externalDBID=n%2Fa&rft.externalDocID=24272923
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0960-1481&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0960-1481&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0960-1481&client=summon