Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting
Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained...
Saved in:
| Published in: | IEEE transaction on neural networks and learning systems Vol. 23; no. 11; pp. 1805 - 1815 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York, NY
IEEE
01.11.2012
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 2162-237X, 2162-2388, 2162-2388 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature. |
|---|---|
| AbstractList | Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature. Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature.Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature. |
| Author | Bonanno, F. Capizzi, G. Napoli, C. |
| Author_xml | – sequence: 1 givenname: G. surname: Capizzi fullname: Capizzi, G. email: gcapizzi@diees.unict.it organization: Dept. of Electr., Electron., & Inf. Eng., Univ. of Catania, Catania, Italy – sequence: 2 givenname: C. surname: Napoli fullname: Napoli, C. email: chnapoli@gmail.com organization: Dept. of Phys. & Astron., Univ. of Catania, Catania, Italy – sequence: 3 givenname: F. surname: Bonanno fullname: Bonanno, F. email: francesco.bonanno2@ingpec.eu organization: Dept. of Electr., Electron., & Inf. Eng., Univ. of Catania, Catania, Italy |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26593205$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/24808074$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkk9r3DAQxUVJaP40X6CFYiiBXryVZEm2jmVJ0sCyhWxKexOyPG6VeqVEkjf021cbbxLIodVlhuH3htHjHaE95x0g9JbgGSFYfrpeLherGcWEziglgjPxCh3mhpa0apq9p77-cYBOYrzB-QnMBZOv0QFlDW5wzQ7R3aVzfqOT3UCxAuNdV16Ag5An3hXf9QYGSLGYexdTGM00telXcQVmDAFcKpYwBj3kku59-B2L3odi5Qcdiivd2WnRuQ9gdEzW_XyD9ns9RDjZ1WP07fzsev6lXHy9uJx_XpSGEZZKUTcCGMhW14b2nGkJRBPW8o4BaTk1tcYidwRj1mgDhGpJO8YAg2klFtUx-jjtvQ3-boSY1NpGA8OgHfgxKsJpxTCTNf8_WhEueJU9zuiHF-iNH4PLH1GEkKqWhMg6U-931NiuoVO3wa51-KMefc_A6Q7Q0eihD9oZG585wWVF8fayZuJM8DEG6JWx6cHSFLQdFMFqmwb1kAa1TYPapSFL6Qvp4_Z_it5NIgsATwKRbxFcVH8Bdwu_tA |
| CODEN | ITNNAL |
| CitedBy_id | crossref_primary_10_1016_j_enconman_2020_112909 crossref_primary_10_1016_j_rser_2013_08_055 crossref_primary_10_3390_app121910056 crossref_primary_10_1016_j_rser_2017_08_017 crossref_primary_10_3390_app10134588 crossref_primary_10_1016_j_asoc_2017_08_057 crossref_primary_10_1016_j_jclepro_2020_120357 crossref_primary_10_1109_TSMC_2021_3093519 crossref_primary_10_1016_j_bspc_2023_104675 crossref_primary_10_1177_1687814017715983 crossref_primary_10_1007_s00202_023_02134_5 crossref_primary_10_1007_s11390_017_1746_7 crossref_primary_10_1016_j_apenergy_2024_123750 crossref_primary_10_3390_app9183670 crossref_primary_10_1007_s11063_018_9893_6 crossref_primary_10_1016_j_neunet_2016_07_004 crossref_primary_10_3390_en10070971 crossref_primary_10_1109_TII_2018_2882598 crossref_primary_10_3390_en18010205 crossref_primary_10_1016_j_pecs_2018_10_003 crossref_primary_10_3390_su10124863 crossref_primary_10_1007_s10479_018_2879_y crossref_primary_10_3390_en15186624 crossref_primary_10_1016_j_renene_2021_02_161 crossref_primary_10_1080_10298436_2025_2472839 crossref_primary_10_1109_TNNLS_2015_2480709 crossref_primary_10_3390_en11040819 crossref_primary_10_1016_j_bica_2018_07_019 crossref_primary_10_1155_2015_807527 crossref_primary_10_1016_j_jastp_2015_09_014 crossref_primary_10_1109_TII_2020_3000184 crossref_primary_10_1109_TSG_2017_2718241 crossref_primary_10_1007_s11063_020_10350_4 crossref_primary_10_1016_j_renene_2016_01_058 crossref_primary_10_1016_j_automatica_2019_108688 crossref_primary_10_1016_j_apenergy_2016_01_130 crossref_primary_10_1109_ACCESS_2021_3122826 crossref_primary_10_1109_TSTE_2014_2313600 crossref_primary_10_1016_j_seta_2021_101248 crossref_primary_10_1049_iet_rpg_2016_1036 crossref_primary_10_1109_JSYST_2014_2305494 crossref_primary_10_1109_TNNLS_2021_3098866 crossref_primary_10_1038_s41598_024_54181_y crossref_primary_10_1016_j_apenergy_2018_02_160 crossref_primary_10_1080_02522667_2022_2042093 crossref_primary_10_1109_JSYST_2015_2409888 crossref_primary_10_1016_j_jocs_2017_04_008 crossref_primary_10_1007_s40095_022_00501_9 crossref_primary_10_1016_j_renene_2018_06_022 crossref_primary_10_3390_su14084427 crossref_primary_10_1049_iet_rpg_2018_5779 crossref_primary_10_3390_en13153987 crossref_primary_10_1016_j_solener_2017_08_086 crossref_primary_10_1007_s00704_018_2627_x crossref_primary_10_1109_TNNLS_2013_2276053 crossref_primary_10_1109_ACCESS_2019_2932999 crossref_primary_10_3390_su132111893 crossref_primary_10_1109_TNNLS_2019_2946414 crossref_primary_10_1016_j_eswa_2022_117831 crossref_primary_10_1016_j_eswa_2022_119215 crossref_primary_10_1016_j_energy_2018_08_207 crossref_primary_10_1109_TNNLS_2025_3529995 crossref_primary_10_3390_fractalfract7010093 crossref_primary_10_1016_j_enconman_2018_12_103 crossref_primary_10_1109_JSYST_2020_3007184 crossref_primary_10_1155_2014_858260 crossref_primary_10_1080_01430750_2022_2068069 crossref_primary_10_1016_j_pecs_2013_06_002 crossref_primary_10_3390_su12052011 crossref_primary_10_3390_su142417005 crossref_primary_10_1049_iet_rpg_2018_5649 crossref_primary_10_1016_j_micpro_2020_103001 crossref_primary_10_1109_TNNLS_2019_2918795 crossref_primary_10_1016_j_jclepro_2022_135680 crossref_primary_10_1109_TSTE_2018_2888548 crossref_primary_10_1007_s11069_024_06837_1 crossref_primary_10_1007_s12667_016_0218_4 crossref_primary_10_1080_01430750_2018_1507929 crossref_primary_10_55544_jrasb_3_6_22 crossref_primary_10_1007_s00521_017_3225_z crossref_primary_10_1016_j_jclepro_2019_01_096 crossref_primary_10_3390_electronics9101717 crossref_primary_10_1109_TII_2020_2996235 crossref_primary_10_1016_j_energy_2022_123785 crossref_primary_10_1109_TII_2020_2987096 crossref_primary_10_1049_iet_gtd_2015_0175 crossref_primary_10_1016_j_ijepes_2020_106041 |
| Cites_doi | 10.1002/047084535X 10.1109/ICASSP.1998.681737 10.1080/09540098908915631 10.1109/TPWRS.2006.873421 10.1007/978-3-642-29347-4_3 10.1109/TSTE.2010.2076359 10.1109/18.57199 10.1109/72.279188 10.1017/S1743921311006806 10.1109/TNNLS.2012.2198074 10.1109/72.165591 10.1109/TSTE.2011.2157540 10.1016/0741-983X(89)90076-3 10.1016/0038-092X(92)90068-L 10.1002/0471427950 10.1109/TNN.2011.2170180 10.1016/S0960-1481(98)00068-8 10.1016/0927-0248(92)90125-9 10.1117/12.205451 10.1162/neco.1989.1.2.270 10.1016/0038-092X(91)90023-P 10.1016/S0038-092X(98)00078-4 10.1016/S0038-092X(99)00017-1 10.1137/S0036141095289051 10.1016/0168-1923(94)90103-1 10.1109/TNN.2010.2041068 10.1109/CIFER.2003.1196279 10.1017/S174392131100679X 10.1109/TSTE.2011.2159254 10.1109/TNN.2010.2066285 10.1016/0167-2789(86)90244-7 |
| ContentType | Journal Article |
| Copyright | 2014 INIST-CNRS Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Nov 2012 |
| Copyright_xml | – notice: 2014 INIST-CNRS – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Nov 2012 |
| DBID | 97E RIA RIE AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| DOI | 10.1109/TNNLS.2012.2216546 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Calcium & Calcified Tissue Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Materials Business File Aerospace Database Engineered Materials Abstracts Biotechnology Research Abstracts Chemoreception Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts Neurosciences Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts Corrosion Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Materials Research Database MEDLINE Engineering Research Database |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science Mathematics Applied Sciences Statistics |
| EISSN | 2162-2388 |
| EndPage | 1815 |
| ExternalDocumentID | 2794254901 24808074 26593205 10_1109_TNNLS_2012_2216546 6320656 |
| Genre | orig-research Journal Article |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IFIPE IPLJI JAVBF M43 MS~ O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION IQODW RIG CGR CUY CVF ECM EIF NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 |
| ID | FETCH-LOGICAL-c414t-6786e4e9ba7c2f54a9e1a14b5d4e1b52c7a06e1b10048ace12a92d44e0ecb9063 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 136 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000310370300011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2162-237X 2162-2388 |
| IngestDate | Sat Sep 27 17:06:26 EDT 2025 Tue Oct 07 09:32:47 EDT 2025 Sun Jun 29 12:41:52 EDT 2025 Mon Jul 21 05:54:45 EDT 2025 Wed Apr 02 07:15:13 EDT 2025 Sat Nov 29 01:39:46 EST 2025 Tue Nov 18 22:30:52 EST 2025 Tue Aug 26 17:19:20 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 11 |
| Keywords | Recurrent neural nets Wind Correlation wavelet theory recurrent neural networks (RNNs) Methodological time series Time series Inverse transformation Neural network Experimental study Mean square error Photovoltaic power plant Wavelet transformation University Humidity prediction Observation data photovoltaic (PV) module Solar radiation Root mean square value Meteorology second-generation wavelets |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html CC BY 4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c414t-6786e4e9ba7c2f54a9e1a14b5d4e1b52c7a06e1b10048ace12a92d44e0ecb9063 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PMID | 24808074 |
| PQID | 1113791197 |
| PQPubID | 85436 |
| PageCount | 11 |
| ParticipantIDs | pubmed_primary_24808074 proquest_journals_1113791197 proquest_miscellaneous_1315653546 ieee_primary_6320656 proquest_miscellaneous_1523404975 crossref_citationtrail_10_1109_TNNLS_2012_2216546 crossref_primary_10_1109_TNNLS_2012_2216546 pascalfrancis_primary_26593205 |
| PublicationCentury | 2000 |
| PublicationDate | 2012-11-01 |
| PublicationDateYYYYMMDD | 2012-11-01 |
| PublicationDate_xml | – month: 11 year: 2012 text: 2012-11-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | New York, NY |
| PublicationPlace_xml | – name: New York, NY – name: United States – name: Piscataway |
| PublicationTitle | IEEE transaction on neural networks and learning systems |
| PublicationTitleAbbrev | TNNLS |
| PublicationTitleAlternate | IEEE Trans Neural Netw Learn Syst |
| PublicationYear | 2012 |
| Publisher | IEEE Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: Institute of Electrical and Electronics Engineers – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref34 ref12 ref37 ref15 ref36 ref14 ref31 mellit (ref20) 2004; 1 ref30 ref10 ref1 ref39 ref17 ref38 ref19 ref18 haykin (ref28) 2008 hokoi (ref3) 1990; 2 bonanno (ref32) 2010 yan (ref35) 2012; 23 capizzi (ref33) 2012 ref24 mallat (ref22) 2009 ref23 ref26 ref25 strang (ref16) 1996 ref21 kalogirou (ref11) 2002 ref27 ref29 ref8 ref7 ref9 ref4 mellit (ref2) 2003; 1 ref6 ref5 |
| References_xml | – ident: ref27 doi: 10.1002/047084535X – ident: ref29 doi: 10.1109/ICASSP.1998.681737 – ident: ref26 doi: 10.1080/09540098908915631 – ident: ref18 doi: 10.1109/TPWRS.2006.873421 – start-page: 21 year: 2012 ident: ref33 article-title: An innovative hybrid neuro-wavelet method for reconstruction of missing data in astronomical photometric surveys publication-title: Proc 11th Int Conf Artif Intell Soft Comput doi: 10.1007/978-3-642-29347-4_3 – ident: ref17 doi: 10.1109/TSTE.2010.2076359 – start-page: 586 year: 2010 ident: ref32 article-title: A wavelet based prediction for wind and solar energy for long-term simulation of integrated generation systems publication-title: Proc Int Symp Power Electron Elect Drives Autom Motion – ident: ref13 doi: 10.1109/18.57199 – ident: ref24 doi: 10.1109/72.279188 – ident: ref31 doi: 10.1017/S1743921311006806 – volume: 23 start-page: 1028 year: 2012 ident: ref35 article-title: Toward automatic time-series forecasting using neural networks publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2012.2198074 – year: 2008 ident: ref28 publication-title: Neural Networks and Learning Machines – ident: ref15 doi: 10.1109/72.165591 – ident: ref4 doi: 10.1109/TSTE.2011.2157540 – ident: ref7 doi: 10.1016/0741-983X(89)90076-3 – ident: ref5 doi: 10.1016/0038-092X(92)90068-L – ident: ref34 doi: 10.1002/0471427950 – ident: ref37 doi: 10.1109/TNN.2011.2170180 – ident: ref9 doi: 10.1016/S0960-1481(98)00068-8 – year: 1996 ident: ref16 publication-title: Wavelets and Filter Banks – year: 2009 ident: ref22 publication-title: The Sparse Way – ident: ref1 doi: 10.1016/0927-0248(92)90125-9 – ident: ref14 doi: 10.1117/12.205451 – ident: ref25 doi: 10.1162/neco.1989.1.2.270 – ident: ref6 doi: 10.1016/0038-092X(91)90023-P – ident: ref8 doi: 10.1016/S0038-092X(98)00078-4 – ident: ref10 doi: 10.1016/S0038-092X(99)00017-1 – volume: 1 start-page: 353 year: 2003 ident: ref2 article-title: Modelling of sizing the photovoltaic system parameters using artificial neural network publication-title: Proc IEEE Conf Control Appl – ident: ref21 doi: 10.1137/S0036141095289051 – ident: ref12 doi: 10.1016/0168-1923(94)90103-1 – ident: ref39 doi: 10.1109/TNN.2010.2041068 – ident: ref36 doi: 10.1109/CIFER.2003.1196279 – volume: 1 start-page: 95 year: 2004 ident: ref20 article-title: An adaptive radial basis function, networks with IIR filter for sizing of stand-alone PV systems publication-title: Proc 10th IFAC/INFORS/IMACS/IFIP Symp Large Scale Syst Theory Appl – ident: ref30 doi: 10.1017/S174392131100679X – start-page: 1 year: 2002 ident: ref11 article-title: Prediction of maximum solar radiation using artificial neural networks publication-title: Proc World Renew Energy Congr – ident: ref19 doi: 10.1109/TSTE.2011.2159254 – volume: 2 start-page: 245 year: 1990 ident: ref3 article-title: Stochastic models of solar radiation and outdoor temperature publication-title: Proc Annu Conf Amer Soc Heat Refrigerat Air-Condition Eng – ident: ref38 doi: 10.1109/TNN.2010.2066285 – ident: ref23 doi: 10.1016/0167-2789(86)90244-7 |
| SSID | ssj0000605649 |
| Score | 2.4678712 |
| Snippet | Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic... |
| SourceID | proquest pubmed pascalfrancis crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1805 |
| SubjectTerms | Algorithms Applied sciences Artificial intelligence Computer science; control theory; systems Connectionism. Neural networks Earth, ocean, space Energy Exact sciences and technology External geophysics Forecasting Humans Inference from stochastic processes; time series analysis Mathematics Meteorology Methodological time series Natural energy Neural Networks (Computer) Neurons photovoltaic (PV) module Photovoltaic conversion prediction Probability and statistics Radiative transfer. Solar radiation recurrent neural networks (RNNs) Sciences and techniques of general use second-generation wavelets Solar Energy Solar radiation Statistics Studies Time series analysis Vectors Wavelet Analysis wavelet theory Wavelet transforms |
| Title | Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting |
| URI | https://ieeexplore.ieee.org/document/6320656 https://www.ncbi.nlm.nih.gov/pubmed/24808074 https://www.proquest.com/docview/1113791197 https://www.proquest.com/docview/1315653546 https://www.proquest.com/docview/1523404975 |
| Volume | 23 |
| WOSCitedRecordID | wos000310370300011&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: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2162-2388 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000605649 issn: 2162-237X databaseCode: RIE dateStart: 20120101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3daxQxEB_a4oMvtrV-rLZHBN90283HJpfHIhaFskiv2ntbstksLcidutf-_c5ksysFK_gWSEKyOzNkJpn5_QDe6nknnQgqD8o3udIY7jjX2txLqRt6xyrihdu3c1NV8-XSftmC91MtTAghJp-FY2rGt_x27W_pquxES4Enpt6GbWPMUKs13acU6Jfr6O0KrkUupFmONTKFPbmsqvMFJXKJYyFiAQ-hACsCVTTq3pEUOVYoQ9L1-JO6gd3iYfczHkNnu__3AXvwJLmb7HTQj33YCqunsDtSObBk2Qfw83NiR70LbEExcpsPgNQkN3bliJ5i0zOi9xwBZ9nVzeaaXdB9PSE8MYL5wKWqIa-8Z-gNswUFzuyC8A_iFCIC9a6nVOtn8PXs4-WHT3liY8i94mqT46mmgwq2ccaLrlTOBu64aspWBd6UwhtXaGwRBN3c-cCFs6JVKhTBNxY9oeews1qvwktg0nZt49Gz6XhEtHcEaq8ajOWk59a4DPgokNonqHJizPhex5ClsHWUZ03yrJM8M3g3zfkxAHX8c_QBSWcamQSTweye3Kd-oUt0dIsyg8NREepk7D0FUdJYeo_N4M3UjWZKby9uFda3OEZioFzKuPSDY0ohFUZsBpd5MSjZnw0kXX31942_hsf0eUOR5CHsoCqEI3jk7zY3_a8Z2styPov28hsTiw9k |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9QwDLbGQIIXBoxBYYwg8Qbdmh9NL49o2rSJo0K7g91blaauNmm6A3rb30-cpkWTGBJvkZIoae0odmx_H8B7PWmlFahSVK5OlfbujrWNSZ2UuqY4VhYe3L5Pi7KcLBbm6wZ8HGthEDEkn-E-NUMsv1m5a3oqO9BS-BtT34P7uVKC99Va44tK5i1zHexdwbVIhSwWQ5VMZg7mZTmdUSqX2BcilPAQDrAiWMVC3bqUAssK5Ujazv-mtue3uNsADRfR8db_fcITeBwNTvap15CnsIHLZ7A1kDmweLa34edp5Ee9QTYjL7lJe0hqkhw7t0RQse4YEXwOkLPs_HJ9wc7oxZ4wnhgBffilyj6zvGPeHmYzcp3ZGSEghClEBepsR8nWz-Hb8dH88CSNfAypU1ytU3-vaVRoals40ebKGuSWqzpvFPI6F66wmfYtAqGbWIdcWCMapTBDVxtvC-3A5nK1xJfApGmb2nnbpuUB094SrL2qvTcnHTeFTYAPAqlcBCsnzoyrKjgtmamCPCuSZxXlmcCHcc6PHqrjn6O3STrjyCiYBPZuyX3sFzr3pm6WJ7A7KEIVj3tHbpQsDEVkE3g3dvuDStEXu8TVtR8jvaucy7D0nWNyIZX32Qq_zIteyf5sIOrqq79v_C08PJl_mVbT0_Lza3hEn9qXTO7CplcLfAMP3M36svu1F07Nb-kJEcM |
| 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=Innovative+Second-Generation+Wavelets+Construction+With+Recurrent+Neural+Networks+for+Solar+Radiation+Forecasting&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Capizzi%2C+G.&rft.au=Napoli%2C+C.&rft.au=Bonanno%2C+F.&rft.date=2012-11-01&rft.issn=2162-237X&rft.eissn=2162-2388&rft.volume=23&rft.issue=11&rft.spage=1805&rft.epage=1815&rft_id=info:doi/10.1109%2FTNNLS.2012.2216546&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TNNLS_2012_2216546 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-237X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-237X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-237X&client=summon |