Estimation of sky luminance in the tropics using artificial neural networks: Modeling and performance comparison with the CIE model
An artificial neural network (ANN) model for estimating sky luminance was developed. A 3-year period (2007–2009) of sky luminance data obtained from measurements at Nakhon Pathom (13.82°N, 100.04°E) and a 1-year period (2008) of the same type of data at Songkhla (7.20°N, 100.60°E), Thailand were use...
Saved in:
| Published in | Applied energy Vol. 88; no. 3; pp. 840 - 847 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Kidlington
Elsevier Ltd
01.03.2011
Elsevier |
| Series | Applied Energy |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0306-2619 1872-9118 |
| DOI | 10.1016/j.apenergy.2010.09.004 |
Cover
| Abstract | An artificial neural network (ANN) model for estimating sky luminance was developed. A 3-year period (2007–2009) of sky luminance data obtained from measurements at Nakhon Pathom (13.82°N, 100.04°E) and a 1-year period (2008) of the same type of data at Songkhla (7.20°N, 100.60°E), Thailand were used in this study. The ANN model was trained using a back propagation algorithm, based on 2
years data (2007–2008) at Nakhon Pathom for clear, partly cloudy and overcast skies. The trained ANN model was used to predict sky luminance at Nakhon Pathom for the year 2009 for the case of clear, partly cloudy and overcast skies. The results were compared with those of the CIE model. It was found that the ANN model performed better than CIE models for most cases. The ANN model trained with Nakhon Pathom data were also used to predict sky luminance at Songkhla and satisfactory results were obtained. |
|---|---|
| AbstractList | An artificial neural network (ANN) model for estimating sky luminance was developed. A 3-year period (2007-2009) of sky luminance data obtained from measurements at Nakhon Pathom (13.82°N, 100.04°E) and a 1-year period (2008) of the same type of data at Songkhla (7.20°N, 100.60°E), Thailand were used in this study. The ANN model was trained using a back propagation algorithm, based on 2 years data (2007-2008) at Nakhon Pathom for clear, partly cloudy and overcast skies. The trained ANN model was used to predict sky luminance at Nakhon Pathom for the year 2009 for the case of clear, partly cloudy and overcast skies. The results were compared with those of the CIE model. It was found that the ANN model performed better than CIE models for most cases. The ANN model trained with Nakhon Pathom data were also used to predict sky luminance at Songkhla and satisfactory results were obtained. An artificial neural network (ANN) model for estimating sky luminance was developed. A 3-year period (2007–2009) of sky luminance data obtained from measurements at Nakhon Pathom (13.82°N, 100.04°E) and a 1-year period (2008) of the same type of data at Songkhla (7.20°N, 100.60°E), Thailand were used in this study. The ANN model was trained using a back propagation algorithm, based on 2 years data (2007–2008) at Nakhon Pathom for clear, partly cloudy and overcast skies. The trained ANN model was used to predict sky luminance at Nakhon Pathom for the year 2009 for the case of clear, partly cloudy and overcast skies. The results were compared with those of the CIE model. It was found that the ANN model performed better than CIE models for most cases. The ANN model trained with Nakhon Pathom data were also used to predict sky luminance at Songkhla and satisfactory results were obtained. An artificial neural network (ANN) model for estimating sky luminance was developed. A 3-year period (2007–2009) of sky luminance data obtained from measurements at Nakhon Pathom (13.82°N, 100.04°E) and a 1-year period (2008) of the same type of data at Songkhla (7.20°N, 100.60°E), Thailand were used in this study. The ANN model was trained using a back propagation algorithm, based on 2years data (2007–2008) at Nakhon Pathom for clear, partly cloudy and overcast skies. The trained ANN model was used to predict sky luminance at Nakhon Pathom for the year 2009 for the case of clear, partly cloudy and overcast skies. The results were compared with those of the CIE model. It was found that the ANN model performed better than CIE models for most cases. The ANN model trained with Nakhon Pathom data were also used to predict sky luminance at Songkhla and satisfactory results were obtained. An artificial neural network (ANN) model for estimating sky luminance was developed. A 3-year period (2007-2009) of sky luminance data obtained from measurements at Nakhon Pathom (13.82 degree N, 100.04 degree E) and a 1-year period (2008) of the same type of data at Songkhla (7.20 degree N, 100.60 degree E), Thailand were used in this study. The ANN model was trained using a back propagation algorithm, based on 2 years data (2007-2008) at Nakhon Pathom for clear, partly cloudy and overcast skies. The trained ANN model was used to predict sky luminance at Nakhon Pathom for the year 2009 for the case of clear, partly cloudy and overcast skies. The results were compared with those of the CIE model. It was found that the ANN model performed better than CIE models for most cases. The ANN model trained with Nakhon Pathom data were also used to predict sky luminance at Songkhla and satisfactory results were obtained. |
| Author | Plaon, Piyanuch Janjai, Serm |
| Author_xml | – sequence: 1 givenname: Serm surname: Janjai fullname: Janjai, Serm email: serm@su.ac.th – sequence: 2 givenname: Piyanuch surname: Plaon fullname: Plaon, Piyanuch |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23825426$$DView record in Pascal Francis http://econpapers.repec.org/article/eeeappene/v_3a88_3ay_3a2011_3ai_3a3_3ap_3a840-847.htm$$DView record in RePEc |
| BookMark | eNqFkk9vEzEQxVeoSKSFrwC-ILgk-M-ud404gKIARUUcoGfL68wmTnftre20ypkvziRpOXAgh9mR7N-8Wfm98-LMBw9F8ZLRGaNMvtvMzAge4mo34xQPqZpRWj4pJqyp-VQx1pwVEyqonHLJ1LPiPKUNpZQzTifF70XKbjDZBU9CR9LNjvTbwXnjLRDnSV4DyTGMziayTc6viInZdc460xMP23ho-T7Em_SefA9L6A-QX5IRYhficFCyYRhNdAm33Lu8PsjOLxdk2A88L552pk_w4qFfFNefF7_mX6dXP75czj9dTW0lWJ5KowRrhWpbBpWwrWGAJeq6bKkAyrjkSrQd8Ba46vCu4U2jhGkqW7elseKieHPUHWO43ULKenDJQt8bD2GbdFNVNVO0oki-_S_J6rpmgnKlTqNSCsmFFCWi345ohBGsHiM-fNxpADDj3kF9p_FvG_zssNBLhs1hCaxxf1dS3ZS1XucBxV4_7DXJmr6L-M4u_RXlouFVySVyH46cjSGlCJ22Lh_8ztG4XjOq9ynSG_2YIr1PkaZKY4pwXP4z_rjh5OCr42BngjYr9F5f_0SgwuhJKXmNxMcjAej5nYOok3WAaVm6CDbrZXCnlvwBQMzyNw |
| CODEN | APENDX |
| CitedBy_id | crossref_primary_10_1016_j_rser_2013_12_018 crossref_primary_10_1016_j_energy_2016_02_054 crossref_primary_10_1016_j_apenergy_2015_11_046 crossref_primary_10_1177_14771535241276338 crossref_primary_10_1016_j_solener_2012_06_022 crossref_primary_10_1016_j_apenergy_2024_123480 crossref_primary_10_1016_j_renene_2016_11_008 crossref_primary_10_1080_2093761X_2015_1129369 crossref_primary_10_1016_j_solener_2020_11_051 crossref_primary_10_1080_19401493_2022_2102676 crossref_primary_10_1177_14780771231186259 crossref_primary_10_1016_j_rser_2018_05_060 crossref_primary_10_1016_j_solener_2022_02_053 crossref_primary_10_1016_j_solener_2017_04_010 crossref_primary_10_1016_j_solener_2020_03_104 crossref_primary_10_1109_MCG_2016_67 crossref_primary_10_1080_19401493_2019_1610067 crossref_primary_10_5370_JEET_2015_10_4_1822 crossref_primary_10_1016_j_enbuild_2016_01_027 crossref_primary_10_1016_j_solener_2019_10_072 crossref_primary_10_1080_1463922X_2011_617112 crossref_primary_10_1080_13467581_2020_1752217 crossref_primary_10_1108_SASBE_08_2021_0146 |
| Cites_doi | 10.1016/j.apenergy.2009.09.008 10.1016/j.apenergy.2010.03.004 10.1016/j.buildenv.2008.11.012 10.1016/j.solener.2004.11.013 10.1016/S1364-6826(02)00283-3 10.1177/1365782806070683 10.1016/j.apenergy.2003.11.004 10.1016/j.solener.2004.04.016 10.1016/S0960-1481(98)00068-8 10.1016/0038-092X(93)90017-I 10.1016/j.apenergy.2010.05.006 10.1016/j.jastp.2004.12.008 10.1016/j.apenergy.2008.06.003 10.1016/j.renene.2006.06.004 10.1016/j.apenergy.2009.09.034 10.1016/S0306-2619(03)00137-5 10.1016/j.solener.2009.07.004 10.1016/j.renene.2007.09.012 10.1016/j.enconman.2009.03.035 10.1016/j.apenergy.2010.02.018 10.1016/S0306-2619(02)00016-8 10.1016/0038-092X(90)90055-H 10.1016/j.buildenv.2007.12.009 10.1016/j.apenergy.2009.03.004 10.1016/j.enconman.2008.02.014 10.1016/j.enpol.2008.06.030 10.1016/j.apenergy.2008.09.004 10.1016/S0196-8904(03)00009-8 10.1016/S0167-7012(00)00201-3 10.1191/1477153504li117oa 10.1016/j.energy.2004.05.002 10.1177/096032718101300402 |
| ContentType | Journal Article |
| Copyright | 2010 Elsevier Ltd 2015 INIST-CNRS |
| Copyright_xml | – notice: 2010 Elsevier Ltd – notice: 2015 INIST-CNRS |
| DBID | FBQ AAYXX CITATION IQODW DKI X2L 7S9 L.6 7SU 7TA 8FD C1K FR3 JG9 7ST SOI |
| DOI | 10.1016/j.apenergy.2010.09.004 |
| DatabaseName | AGRIS CrossRef Pascal-Francis RePEc IDEAS RePEc AGRICOLA AGRICOLA - Academic Environmental Engineering Abstracts Materials Business File Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database Materials Research Database Environment Abstracts Environment Abstracts |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic Materials Research Database Engineering Research Database Technology Research Database Materials Business File Environmental Engineering Abstracts Environmental Sciences and Pollution Management Environment Abstracts |
| DatabaseTitleList | Environment Abstracts AGRICOLA Materials Research Database |
| Database_xml | – sequence: 1 dbid: DKI name: RePEc IDEAS url: http://ideas.repec.org/ sourceTypes: Index Database – sequence: 2 dbid: FBQ name: AGRIS url: http://www.fao.org/agris/Centre.asp?Menu_1ID=DB&Menu_2ID=DB1&Language=EN&Content=http://www.fao.org/agris/search?Language=EN sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Environmental Sciences |
| EISSN | 1872-9118 |
| EndPage | 847 |
| ExternalDocumentID | eeeappene_v_3a88_3ay_3a2011_3ai_3a3_3ap_3a840_847_htm 23825426 10_1016_j_apenergy_2010_09_004 US201500066627 S0306261910003685 |
| GeographicLocations | Thailand Far East Asia |
| GeographicLocations_xml | – name: Thailand |
| GroupedDBID | --K --M .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARJD AAXUO AAYOK ABEFU ABFNM ABJNI ABMAC ABTAH 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 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HVGLF HZ~ IHE J1W JARJE JJJVA KOM LY6 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAC SDF SDG SES SEW SPC SPCBC SSR SST SSZ T5K TN5 WUQ ZY4 ~02 ~G- AAHBH AATTM AAXKI ABWVN ACRPL ADNMO AEIPS AFJKZ AKRWK ANKPU BNPGV FBQ SSH AAYWO AAYXX ACLOT ACVFH ADCNI AEUPX AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP APXCP CITATION EFKBS ~HD AFXIZ AGCQF AGRNS IQODW 02 0R 1 8P AAPBV ABPTK ADALY DKI G- HZ IPNFZ K M X2L 7S9 L.6 7SU 7TA 8FD C1K FR3 JG9 7ST SOI |
| ID | FETCH-LOGICAL-c531t-6a931b39bb1e53cba1eba13774b03e0126293bfe2be29feba828893a85c7b4ac3 |
| IEDL.DBID | .~1 |
| ISSN | 0306-2619 |
| IngestDate | Tue Oct 07 09:24:40 EDT 2025 Tue Oct 07 09:24:44 EDT 2025 Sun Sep 28 07:00:49 EDT 2025 Thu Dec 16 09:11:49 EST 2021 Mon Jul 21 09:14:21 EDT 2025 Thu Oct 09 00:34:31 EDT 2025 Thu Apr 24 22:59:29 EDT 2025 Thu Apr 03 09:43:56 EDT 2025 Fri Feb 23 02:30:27 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | The tropics Sky luminance Daylight CIE model Artificial neural network Modeling Backpropagation algorithm Luminance neural networks tropical zone Cloudiness Forecast model |
| Language | English |
| License | CC BY 4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c531t-6a931b39bb1e53cba1eba13774b03e0126293bfe2be29feba828893a85c7b4ac3 |
| Notes | http://dx.doi.org/10.1016/j.apenergy.2010.09.004 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 1663623634 |
| PQPubID | 23462 |
| PageCount | 8 |
| ParticipantIDs | proquest_miscellaneous_855719050 proquest_miscellaneous_1777130299 proquest_miscellaneous_1663623634 repec_primary_eeeappene_v_3a88_3ay_3a2011_3ai_3a3_3ap_3a840_847_htm pascalfrancis_primary_23825426 crossref_citationtrail_10_1016_j_apenergy_2010_09_004 crossref_primary_10_1016_j_apenergy_2010_09_004 fao_agris_US201500066627 elsevier_sciencedirect_doi_10_1016_j_apenergy_2010_09_004 |
| PublicationCentury | 2000 |
| PublicationDate | 2011-03-01 |
| PublicationDateYYYYMMDD | 2011-03-01 |
| PublicationDate_xml | – month: 03 year: 2011 text: 2011-03-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Kidlington |
| PublicationPlace_xml | – name: Kidlington |
| PublicationSeriesTitle | Applied Energy |
| PublicationTitle | Applied energy |
| PublicationYear | 2011 |
| Publisher | Elsevier Ltd Elsevier |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier |
| References | Li, Tsang, Cheung, Tam (b0010) 2010; 87 Pokrowski (b0030) 1929; 30 Chirarattananon (b0005) 2005 Matsuura K, Iwata, T. A model of daylight source for the daylight illuminance calculations on the all weather conditions. In: Spiridonov A. Proceedings of 3rd international daylighting conference; 1990. Kittler R. Luminance models of homogeneous skies for design and energy performance predictions. In: Proceedings of international daylight conference, Long Beach, California, USA: American Society of Heating, Refrigerating and Air-conditioning Engineerings, Inc.; 1989. Jiang (b0150) 2008; 36 Bartzokas, Durala, Kambezidis, Kittler (b0110) 2003; 65 Kittler R. Standardization of outdoor conditions for the calculation of daylight factor with clear skies. In: Proceedings of the conference on sunlight in buildings, Rotterdam, Netherlands; 1967. p. 273–80. Perez, Ineichen, Seals, Michalsky, Stewart (b0065) 1990; 44 Dorvlo, Jervase, Al-Lawati (b0170) 2002; 71 Kocifaj (b0080) 2009; 83 Basheer, Hajmeer (b0200) 2000; 43 Igawa, Koga, Matsuzawa, Nakamura (b0075) 2004; 77 Soares, Oliveira, Boznar, Mlakar, Escobedo, Machado (b0155) 2004; 79 Al-Alawi, Al-Hinai (b0145) 1998; 14 Pattanasethanon, Lertsatitthanakorn, Atthajariyakul, Soponronnarit (b0190) 2008; 49 Reddy, Ranjan (b0140) 2003; 44 Sozen, Arcaklioglu, Ozalp, Kanit (b0160) 2004; 77 Senkal, Kuleli (b0180) 2009; 86 Li (b0095) 2010; 87 CIE, Spatial distribution of daylight-CIE standard general sky, Report No. CIE S011/E:2003, Vienna, Austria: Centre International d’Eclairage; 2003. Bartzokas, Kambezidis, Durala, Kittler (b0115) 2005; 67 Chel, Tiwari, Singh (b0025) 2010; 87 Hontoria, Aguilera, Zufiria (b0135) 2005; 79 Benghanem, Mellit, Alamri (b0185) 2009; 50 Moon, Spencer (b0040) 1942; 37 Perraudeau M. Luminance models. National lighting conference and daylighting colloquium. Cambridge, England: Robinson College; 1988. Janjai, Masiri, Nunez, Laksanaboonsong (b0085) 2008; 43 Alam, Kaushik, Garg (b0165) 2009; 86 Markou, Kambezidis, Bartzokas, Katsoulis, Muneer (b0105) 2005; 30 Wittkopf, Soon (b0125) 2007; 3991 Perez, Seals, Michalsky (b0060) 1993; 50 Tregenza (b0100) 2004; 36 Chirarattananon, Chaiwiwatworakul (b0120) 2007; 32 Kazanasmaz, Gunaydin, Binol (b0195) 2009; 44 Darula, Kittler, Kocifaj (b0015) 2010; 87 De Rosa, Ferraro, Kaliakatsos, Marinelli (b0130) 2010; 87 Bosch, Lopez, Batlles (b0175) 2008; 33 Littlefair (b0045) 1981; 13 Chel, Tiwari, Chandra (b0020) 2009; 86 10.1016/j.apenergy.2010.09.004_b0035 Bartzokas (10.1016/j.apenergy.2010.09.004_b0110) 2003; 65 10.1016/j.apenergy.2010.09.004_b0055 Perez (10.1016/j.apenergy.2010.09.004_b0060) 1993; 50 Benghanem (10.1016/j.apenergy.2010.09.004_b0185) 2009; 50 Dorvlo (10.1016/j.apenergy.2010.09.004_b0170) 2002; 71 Senkal (10.1016/j.apenergy.2010.09.004_b0180) 2009; 86 10.1016/j.apenergy.2010.09.004_b0050 Bartzokas (10.1016/j.apenergy.2010.09.004_b0115) 2005; 67 10.1016/j.apenergy.2010.09.004_b0070 Markou (10.1016/j.apenergy.2010.09.004_b0105) 2005; 30 10.1016/j.apenergy.2010.09.004_b0090 Li (10.1016/j.apenergy.2010.09.004_b0010) 2010; 87 Li (10.1016/j.apenergy.2010.09.004_b0095) 2010; 87 Soares (10.1016/j.apenergy.2010.09.004_b0155) 2004; 79 Wittkopf (10.1016/j.apenergy.2010.09.004_b0125) 2007; 3991 Chirarattananon (10.1016/j.apenergy.2010.09.004_b0005) 2005 Kazanasmaz (10.1016/j.apenergy.2010.09.004_b0195) 2009; 44 Jiang (10.1016/j.apenergy.2010.09.004_b0150) 2008; 36 Reddy (10.1016/j.apenergy.2010.09.004_b0140) 2003; 44 Hontoria (10.1016/j.apenergy.2010.09.004_b0135) 2005; 79 Pattanasethanon (10.1016/j.apenergy.2010.09.004_b0190) 2008; 49 Littlefair (10.1016/j.apenergy.2010.09.004_b0045) 1981; 13 Bosch (10.1016/j.apenergy.2010.09.004_b0175) 2008; 33 Perez (10.1016/j.apenergy.2010.09.004_b0065) 1990; 44 Chirarattananon (10.1016/j.apenergy.2010.09.004_b0120) 2007; 32 Alam (10.1016/j.apenergy.2010.09.004_b0165) 2009; 86 Chel (10.1016/j.apenergy.2010.09.004_b0025) 2010; 87 Igawa (10.1016/j.apenergy.2010.09.004_b0075) 2004; 77 Moon (10.1016/j.apenergy.2010.09.004_b0040) 1942; 37 Janjai (10.1016/j.apenergy.2010.09.004_b0085) 2008; 43 Sozen (10.1016/j.apenergy.2010.09.004_b0160) 2004; 77 Pokrowski (10.1016/j.apenergy.2010.09.004_b0030) 1929; 30 Al-Alawi (10.1016/j.apenergy.2010.09.004_b0145) 1998; 14 De Rosa (10.1016/j.apenergy.2010.09.004_b0130) 2010; 87 Kocifaj (10.1016/j.apenergy.2010.09.004_b0080) 2009; 83 Tregenza (10.1016/j.apenergy.2010.09.004_b0100) 2004; 36 Chel (10.1016/j.apenergy.2010.09.004_b0020) 2009; 86 Basheer (10.1016/j.apenergy.2010.09.004_b0200) 2000; 43 Darula (10.1016/j.apenergy.2010.09.004_b0015) 2010; 87 |
| References_xml | – volume: 49 start-page: 1999 year: 2008 end-page: 2005 ident: b0190 article-title: An accuracy assessment of an empirical sine model, a novel sine model and an artificial neural network model for forecasting illuminance/irradiance on horizontal plane of all sky types at Mahasarakham, Thailand publication-title: Energy Convers Manage – volume: 44 start-page: 1751 year: 2009 end-page: 1757 ident: b0195 article-title: Artificial neural networks to predict daylight illuminance in office buildings publication-title: Build Environ – volume: 43 start-page: 2059 year: 2008 end-page: 2073 ident: b0085 article-title: Modeling sky luminance using satellite data to classify sky conditions publication-title: Build Environ – volume: 44 start-page: 271 year: 1990 end-page: 289 ident: b0065 article-title: Modeling daylight availability and irradiance components publication-title: Solar Energy – volume: 36 start-page: 271 year: 2004 end-page: 281 ident: b0100 article-title: Analysing sly luminance scans to obtain frequency distributions of CIE standard general skies publication-title: Light Res Technol – volume: 86 start-page: 1222 year: 2009 end-page: 1228 ident: b0180 article-title: Estimation of solar radiation over Turkey using artificial neural network and satellite data publication-title: Appl Energy – reference: Kittler R. Luminance models of homogeneous skies for design and energy performance predictions. In: Proceedings of international daylight conference, Long Beach, California, USA: American Society of Heating, Refrigerating and Air-conditioning Engineerings, Inc.; 1989. – volume: 87 start-page: 806 year: 2010 end-page: 813 ident: b0130 article-title: Calculating indoor natural illuminance in overcast sky conditions publication-title: Appl Energy – volume: 50 start-page: 235 year: 1993 end-page: 245 ident: b0060 article-title: All-weather model for sky luminance distribution-preliminary configuration and validation publication-title: Solar Energy – volume: 65 start-page: 113 year: 2003 end-page: 119 ident: b0110 article-title: Sky luminance distribution in Central Europe and the Mediterranean area during the winter period publication-title: J Atmos Sol – Terr Phys – volume: 77 start-page: 273 year: 2004 end-page: 286 ident: b0160 article-title: Use of artificial neural networks for mapping of solar potential in Turkey publication-title: Appl Energy – volume: 30 start-page: 697 year: 1929 ident: b0030 article-title: Uber die Helligkeitsverteilung am Himmel publication-title: Phys Zeitschr – volume: 86 start-page: 554 year: 2009 end-page: 564 ident: b0165 article-title: Assessment of diffuse solar energy under general sky condition using artificial neural network publication-title: Appl Energy – reference: Perraudeau M. Luminance models. National lighting conference and daylighting colloquium. Cambridge, England: Robinson College; 1988. – volume: 79 start-page: 523 year: 2005 end-page: 530 ident: b0135 article-title: An application of the multilayer perceptron: solar radiation maps in Spain publication-title: Solar Energy – year: 2005 ident: b0005 article-title: Building for Energy Efficiency – volume: 36 start-page: 3833 year: 2008 end-page: 3837 ident: b0150 article-title: Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models publication-title: Energy Policy – volume: 32 start-page: 1328 year: 2007 end-page: 1345 ident: b0120 article-title: Distribution of sly luminance and radiance of North Bangkok under standard distributions publication-title: Renew Energy – volume: 13 start-page: 192 year: 1981 end-page: 198 ident: b0045 article-title: The luminance distribution of an average sky publication-title: Light Res Technol – reference: Matsuura K, Iwata, T. A model of daylight source for the daylight illuminance calculations on the all weather conditions. In: Spiridonov A. Proceedings of 3rd international daylighting conference; 1990. – volume: 86 start-page: 2507 year: 2009 end-page: 2519 ident: b0020 article-title: A model for estimation of daylight factor for skylight: an experimental validation using pyramid shape skylight over vault roof mud-house in New Delhi (India) publication-title: Appl Energy – volume: 79 start-page: 201 year: 2004 end-page: 214 ident: b0155 article-title: Modeling hourly diffuse solar-radiation in the city of Sao Paulo using a neural-network technique publication-title: Appl Energy – volume: 77 start-page: 137 year: 2004 end-page: 157 ident: b0075 article-title: Models of sky radiance distribution and sky luminance distribution publication-title: Solar Energy – volume: 37 start-page: 707 year: 1942 end-page: 726 ident: b0040 article-title: Illumination from a non-uniform sky publication-title: Illum Eng – volume: 87 start-page: 3460 year: 2010 end-page: 3466 ident: b0015 article-title: Luminous effectiveness of tubular light-guides in tropics publication-title: Appl Energy – volume: 30 start-page: 1667 year: 2005 end-page: 1674 ident: b0105 article-title: Sky type classification in Central England during winter publication-title: Energy – volume: 14 start-page: 199 year: 1998 end-page: 204 ident: b0145 article-title: An ANN-based approach for predicting global radiation in location with no direct measurement instrumentation publication-title: Renew Energy – volume: 67 start-page: 709 year: 2005 end-page: 718 ident: b0115 article-title: Comparison between winter and summer sky luminance distribution in Central Europe and in the eastern Mediterranean publication-title: J Atmos Sol – Terr Phys – volume: 43 start-page: 3 year: 2000 end-page: 31 ident: b0200 article-title: Artificial neural networks: fundamentals, computing, design, and application publication-title: J Microbiol Methods – volume: 87 start-page: 799 year: 2010 end-page: 805 ident: b0010 article-title: An analysis of light-pipe system via full-scale measurements publication-title: Appl Energy – volume: 50 start-page: 1644 year: 2009 end-page: 1655 ident: b0185 article-title: ANN-based modelling and estimation of daily global solar radiation data: a case study publication-title: Energy Convers Manage – reference: Kittler R. Standardization of outdoor conditions for the calculation of daylight factor with clear skies. In: Proceedings of the conference on sunlight in buildings, Rotterdam, Netherlands; 1967. p. 273–80. – volume: 83 start-page: 1914 year: 2009 end-page: 1922 ident: b0080 article-title: Sky luminance /radiance model with multiple scattering effect publication-title: Solar Energy – volume: 44 start-page: 2519 year: 2003 end-page: 2530 ident: b0140 article-title: Solar resource estimation using artificial neural networks and comparison with other correlation models publication-title: Energy Convers Manage – volume: 33 start-page: 1622 year: 2008 end-page: 1628 ident: b0175 article-title: Daily solar irradiation estimation over a mountainous area using artificial neural networks publication-title: Renew Energy – reference: CIE, Spatial distribution of daylight-CIE standard general sky, Report No. CIE S011/E:2003, Vienna, Austria: Centre International d’Eclairage; 2003. – volume: 71 start-page: 307 year: 2002 end-page: 319 ident: b0170 article-title: Solar radiation estimation using artificial neural networks publication-title: Appl Energy – volume: 3991 start-page: 31 year: 2007 end-page: 51 ident: b0125 article-title: Analysing sly luminance scans and predicting frequent sky patterns in Singapore publication-title: Light Res Technol – volume: 87 start-page: 3037 year: 2010 end-page: 3050 ident: b0025 article-title: A modified model for estimation of daylight factor for skylight integrated with dome roof structure of mud-house in New Delhi (India) publication-title: Appl Energy – volume: 87 start-page: 2109 year: 2010 end-page: 2118 ident: b0095 article-title: A review of daylight illuminance determinations and energy implications publication-title: Appl Energy – volume: 87 start-page: 799 year: 2010 ident: 10.1016/j.apenergy.2010.09.004_b0010 article-title: An analysis of light-pipe system via full-scale measurements publication-title: Appl Energy doi: 10.1016/j.apenergy.2009.09.008 – volume: 87 start-page: 2109 year: 2010 ident: 10.1016/j.apenergy.2010.09.004_b0095 article-title: A review of daylight illuminance determinations and energy implications publication-title: Appl Energy doi: 10.1016/j.apenergy.2010.03.004 – year: 2005 ident: 10.1016/j.apenergy.2010.09.004_b0005 – volume: 44 start-page: 1751 year: 2009 ident: 10.1016/j.apenergy.2010.09.004_b0195 article-title: Artificial neural networks to predict daylight illuminance in office buildings publication-title: Build Environ doi: 10.1016/j.buildenv.2008.11.012 – ident: 10.1016/j.apenergy.2010.09.004_b0090 – volume: 79 start-page: 523 year: 2005 ident: 10.1016/j.apenergy.2010.09.004_b0135 article-title: An application of the multilayer perceptron: solar radiation maps in Spain publication-title: Solar Energy doi: 10.1016/j.solener.2004.11.013 – volume: 65 start-page: 113 year: 2003 ident: 10.1016/j.apenergy.2010.09.004_b0110 article-title: Sky luminance distribution in Central Europe and the Mediterranean area during the winter period publication-title: J Atmos Sol – Terr Phys doi: 10.1016/S1364-6826(02)00283-3 – volume: 3991 start-page: 31 year: 2007 ident: 10.1016/j.apenergy.2010.09.004_b0125 article-title: Analysing sly luminance scans and predicting frequent sky patterns in Singapore publication-title: Light Res Technol doi: 10.1177/1365782806070683 – volume: 79 start-page: 201 year: 2004 ident: 10.1016/j.apenergy.2010.09.004_b0155 article-title: Modeling hourly diffuse solar-radiation in the city of Sao Paulo using a neural-network technique publication-title: Appl Energy doi: 10.1016/j.apenergy.2003.11.004 – volume: 77 start-page: 137 year: 2004 ident: 10.1016/j.apenergy.2010.09.004_b0075 article-title: Models of sky radiance distribution and sky luminance distribution publication-title: Solar Energy doi: 10.1016/j.solener.2004.04.016 – volume: 14 start-page: 199 year: 1998 ident: 10.1016/j.apenergy.2010.09.004_b0145 article-title: An ANN-based approach for predicting global radiation in location with no direct measurement instrumentation publication-title: Renew Energy doi: 10.1016/S0960-1481(98)00068-8 – volume: 50 start-page: 235 issue: 3 year: 1993 ident: 10.1016/j.apenergy.2010.09.004_b0060 article-title: All-weather model for sky luminance distribution-preliminary configuration and validation publication-title: Solar Energy doi: 10.1016/0038-092X(93)90017-I – volume: 87 start-page: 3460 year: 2010 ident: 10.1016/j.apenergy.2010.09.004_b0015 article-title: Luminous effectiveness of tubular light-guides in tropics publication-title: Appl Energy doi: 10.1016/j.apenergy.2010.05.006 – volume: 67 start-page: 709 year: 2005 ident: 10.1016/j.apenergy.2010.09.004_b0115 article-title: Comparison between winter and summer sky luminance distribution in Central Europe and in the eastern Mediterranean publication-title: J Atmos Sol – Terr Phys doi: 10.1016/j.jastp.2004.12.008 – ident: 10.1016/j.apenergy.2010.09.004_b0050 – volume: 86 start-page: 1222 year: 2009 ident: 10.1016/j.apenergy.2010.09.004_b0180 article-title: Estimation of solar radiation over Turkey using artificial neural network and satellite data publication-title: Appl Energy doi: 10.1016/j.apenergy.2008.06.003 – volume: 32 start-page: 1328 year: 2007 ident: 10.1016/j.apenergy.2010.09.004_b0120 article-title: Distribution of sly luminance and radiance of North Bangkok under standard distributions publication-title: Renew Energy doi: 10.1016/j.renene.2006.06.004 – volume: 87 start-page: 806 year: 2010 ident: 10.1016/j.apenergy.2010.09.004_b0130 article-title: Calculating indoor natural illuminance in overcast sky conditions publication-title: Appl Energy doi: 10.1016/j.apenergy.2009.09.034 – volume: 77 start-page: 273 year: 2004 ident: 10.1016/j.apenergy.2010.09.004_b0160 article-title: Use of artificial neural networks for mapping of solar potential in Turkey publication-title: Appl Energy doi: 10.1016/S0306-2619(03)00137-5 – volume: 83 start-page: 1914 year: 2009 ident: 10.1016/j.apenergy.2010.09.004_b0080 article-title: Sky luminance /radiance model with multiple scattering effect publication-title: Solar Energy doi: 10.1016/j.solener.2009.07.004 – volume: 33 start-page: 1622 year: 2008 ident: 10.1016/j.apenergy.2010.09.004_b0175 article-title: Daily solar irradiation estimation over a mountainous area using artificial neural networks publication-title: Renew Energy doi: 10.1016/j.renene.2007.09.012 – volume: 50 start-page: 1644 year: 2009 ident: 10.1016/j.apenergy.2010.09.004_b0185 article-title: ANN-based modelling and estimation of daily global solar radiation data: a case study publication-title: Energy Convers Manage doi: 10.1016/j.enconman.2009.03.035 – ident: 10.1016/j.apenergy.2010.09.004_b0035 – volume: 87 start-page: 3037 year: 2010 ident: 10.1016/j.apenergy.2010.09.004_b0025 article-title: A modified model for estimation of daylight factor for skylight integrated with dome roof structure of mud-house in New Delhi (India) publication-title: Appl Energy doi: 10.1016/j.apenergy.2010.02.018 – volume: 71 start-page: 307 year: 2002 ident: 10.1016/j.apenergy.2010.09.004_b0170 article-title: Solar radiation estimation using artificial neural networks publication-title: Appl Energy doi: 10.1016/S0306-2619(02)00016-8 – volume: 44 start-page: 271 year: 1990 ident: 10.1016/j.apenergy.2010.09.004_b0065 article-title: Modeling daylight availability and irradiance components publication-title: Solar Energy doi: 10.1016/0038-092X(90)90055-H – volume: 43 start-page: 2059 year: 2008 ident: 10.1016/j.apenergy.2010.09.004_b0085 article-title: Modeling sky luminance using satellite data to classify sky conditions publication-title: Build Environ doi: 10.1016/j.buildenv.2007.12.009 – volume: 86 start-page: 2507 year: 2009 ident: 10.1016/j.apenergy.2010.09.004_b0020 article-title: A model for estimation of daylight factor for skylight: an experimental validation using pyramid shape skylight over vault roof mud-house in New Delhi (India) publication-title: Appl Energy doi: 10.1016/j.apenergy.2009.03.004 – volume: 49 start-page: 1999 year: 2008 ident: 10.1016/j.apenergy.2010.09.004_b0190 article-title: An accuracy assessment of an empirical sine model, a novel sine model and an artificial neural network model for forecasting illuminance/irradiance on horizontal plane of all sky types at Mahasarakham, Thailand publication-title: Energy Convers Manage doi: 10.1016/j.enconman.2008.02.014 – volume: 36 start-page: 3833 year: 2008 ident: 10.1016/j.apenergy.2010.09.004_b0150 article-title: Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models publication-title: Energy Policy doi: 10.1016/j.enpol.2008.06.030 – volume: 86 start-page: 554 year: 2009 ident: 10.1016/j.apenergy.2010.09.004_b0165 article-title: Assessment of diffuse solar energy under general sky condition using artificial neural network publication-title: Appl Energy doi: 10.1016/j.apenergy.2008.09.004 – volume: 44 start-page: 2519 year: 2003 ident: 10.1016/j.apenergy.2010.09.004_b0140 article-title: Solar resource estimation using artificial neural networks and comparison with other correlation models publication-title: Energy Convers Manage doi: 10.1016/S0196-8904(03)00009-8 – volume: 43 start-page: 3 year: 2000 ident: 10.1016/j.apenergy.2010.09.004_b0200 article-title: Artificial neural networks: fundamentals, computing, design, and application publication-title: J Microbiol Methods doi: 10.1016/S0167-7012(00)00201-3 – volume: 36 start-page: 271 year: 2004 ident: 10.1016/j.apenergy.2010.09.004_b0100 article-title: Analysing sly luminance scans to obtain frequency distributions of CIE standard general skies publication-title: Light Res Technol doi: 10.1191/1477153504li117oa – ident: 10.1016/j.apenergy.2010.09.004_b0055 – volume: 30 start-page: 697 issue: 20 year: 1929 ident: 10.1016/j.apenergy.2010.09.004_b0030 article-title: Uber die Helligkeitsverteilung am Himmel publication-title: Phys Zeitschr – ident: 10.1016/j.apenergy.2010.09.004_b0070 – volume: 30 start-page: 1667 issue: 9 year: 2005 ident: 10.1016/j.apenergy.2010.09.004_b0105 article-title: Sky type classification in Central England during winter publication-title: Energy doi: 10.1016/j.energy.2004.05.002 – volume: 13 start-page: 192 issue: 4 year: 1981 ident: 10.1016/j.apenergy.2010.09.004_b0045 article-title: The luminance distribution of an average sky publication-title: Light Res Technol doi: 10.1177/096032718101300402 – volume: 37 start-page: 707 year: 1942 ident: 10.1016/j.apenergy.2010.09.004_b0040 article-title: Illumination from a non-uniform sky publication-title: Illum Eng |
| SSID | ssj0002120 |
| Score | 2.1042278 |
| Snippet | An artificial neural network (ANN) model for estimating sky luminance was developed. A 3-year period (2007–2009) of sky luminance data obtained from... An artificial neural network (ANN) model for estimating sky luminance was developed. A 3-year period (2007-2009) of sky luminance data obtained from... |
| SourceID | proquest repec pascalfrancis crossref fao elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 840 |
| SubjectTerms | algorithms Artificial neural network Artificial neural networks Back propagation algorithms CIE model cloud cover Daylight Earth atmosphere Earth, ocean, space Estimating Exact sciences and technology External geophysics Learning theory lighting Mathematical models Meteorology Modeling Neural networks Radiative transfer. Solar radiation Sky Sky luminance Sky luminance Daylight Modeling Artificial neural network The tropics CIE model Thailand The tropics Tropics |
| Title | Estimation of sky luminance in the tropics using artificial neural networks: Modeling and performance comparison with the CIE model |
| URI | https://dx.doi.org/10.1016/j.apenergy.2010.09.004 http://econpapers.repec.org/article/eeeappene/v_3a88_3ay_3a2011_3ai_3a3_3ap_3a840-847.htm https://www.proquest.com/docview/1663623634 https://www.proquest.com/docview/1777130299 https://www.proquest.com/docview/855719050 |
| Volume | 88 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Baden-Württemberg Complete Freedom Collection (Elsevier) customDbUrl: eissn: 1872-9118 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002120 issn: 0306-2619 databaseCode: GBLVA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier ScienceDirect customDbUrl: eissn: 1872-9118 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002120 issn: 0306-2619 databaseCode: .~1 dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier ScienceDirect customDbUrl: eissn: 1872-9118 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002120 issn: 0306-2619 databaseCode: ACRLP dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals [SCFCJ] - NZ customDbUrl: eissn: 1872-9118 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002120 issn: 0306-2619 databaseCode: AIKHN dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVLSH databaseName: Elsevier Journals customDbUrl: mediaType: online eissn: 1872-9118 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0002120 issn: 0306-2619 databaseCode: AKRWK dateStart: 19750101 isFulltext: true providerName: Library Specific Holdings |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07b9swECbSZGmHok0bRH0YLNBVsSRSr26B68Bu0CypgWwEKZ9SpakkWEqBLF36x3tHSX4MaYYOtGDzKNB3x-Mn6h6MfQQfgmQpfNeXIFwZxuAmGo2hERK7YpmlPsUOf72IZgv55Sq82mOTIRaG3Cp729_ZdGut-1_GPTfHdVGMLwntEv73bVKVhALNpYypisHJ742bR9CnZkRil6i3ooRvTnQNNsKud_Gi1JXyoQ3qSa4r8pzUDTIv76pe7MDSgxXUkG3tTmcv2PMeVvLTbuYv2R6Uh-zZVrLBQ3Y03cS0IWm_qJtX7M8Ul3kXwcirnDc_7jlaLJuJA3hRcoSIvF1VdZE1nNzkrzkxqMs8wSkfpr1Yb_LmE6fiareWqFzyehOWwLN1xUNOh7_2tpP5lNtaPK_Z4mz6bTJz-9oMboartnUjnQrfiNQYH0KRGe0DNoFg0ngCcNeLEEeYHAIDQZpjHz7ZITTSSZjFRupMHLH9sirhmPE8NRJBg4lzLWS6zBNEHBI0GgtPL_0cHBYOAlFZn7ic6mfcqsFD7UYNglQkSOWlCgXpsPF6XN2l7nh0RDrIW-0oocL95dGxx6ggSl8jI9XiMqCTJMJzURA7bLSjNevZIFzC5_MgctiHQY0Urm56ZaNLqO4a5SMgRIAaCfkPmjiO6fVzmjqMP0CThGGM0C_0HDaxarqeAwDomv6P-qVQPAl-3GMjXIiXApvAVlOf9BRiGfW9_fnmPxj1lj3tzuXJj-8d229Xd_AegV1rRnbljtjB6fx8doHfPp_P_wIj50-6 |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07b9swED7kMbQdijZtEPWRskBXxZZIvboVhgOnTbIkBrIRpHxKnaayYDkFsnTpH-8dJfkxpBk60ALMo0HfHY8fqXsAfMIAw3QiAz9QKH0VJeinhoyhlYq6EpVnAccOn53Ho7H6ehVdbcGgi4Vht8rW9jc23Vnr9ptey81eNZ32LhjtMv4PXFKVNNqGXRWFCZ_Ajn6v_DzCNjcjUftMvhYmfHNkKnQhdq2PF-euVA_tUNuFmbHrpKmJe0VT9mIDl-7OscJ8bXs6fgHPW1wpvjRTfwlbWO7Bs7Vsg3uwP1wFtRFpu6rrV_BnSOu8CWEUs0LUP-4FmSyXigPFtBSEEcViPqumeS3YT_5aMIea1BOCE2K6h3Mnrz8Lrq5264jKiahWcQkiX5Y8FHz76352cDIUrhjPaxgfDy8HI78tzuDntGwXfmwyGViZWRtgJHNrAqQmCU3avkTa9mICErbA0GKYFdRHRzvCRiaN8sQqk8t92ClnJR6AKDKrCDXYpDBSZZMiJcih0JC16JtJUKAHUScQnbeZy7mAxq3uXNRudCdIzYLU_UyTID3oLcdVTe6OR0dknbz1hhZq2mAeHXtACqLNNTFSjy9CvkpiQBeHiQeHG1qznA3hJTqgh7EHHzs10rS8-Z2NKXF2V-uAECEh1Fiqf9AkScLvn7PMA_EATRpFCWG_qO_BwKnpcg6IaCr-P_qXJvGk9HFPjYEhPabUJLWK-1RfE5jR3xc_3_wHoz7Ak9Hl2ak-PTn_9haeNpf07NT3DnYW8zt8TyhvYQ_dKv4LlhpQVg |
| 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=Estimation+of+sky+luminance+in+the+tropics+using+artificial+neural+networks%3A+Modeling+and+performance+comparison+with+the+CIE+model&rft.jtitle=Applied+energy&rft.au=Janjai%2C+Serm&rft.au=Plaon%2C+Piyanuch&rft.date=2011-03-01&rft.issn=0306-2619&rft.volume=88&rft.issue=3+p.840-847&rft.spage=840&rft.epage=847&rft_id=info:doi/10.1016%2Fj.apenergy.2010.09.004&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0306-2619&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0306-2619&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0306-2619&client=summon |