Solar Irradiation Prediction Hybrid Framework Using Regularized Convolutional BiLSTM-Based Autoencoder Approach

Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE access Ročník 11; s. 131362 - 131375
Hlavní autori:	Chiranjeevi, Madderla, Karlamangal, Skandha, Moger, Tukaram, Jena, Debashisha
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Autoencoder Autoregressive processes Benchmarks BiLSTM convolution neural network Convolutional neural networks Decoding Encoding Forecasting Irradiance Irradiation Long short term memory Mathematical models Model accuracy Performance measurement Predictive models Radiation effects Representations Solar power generation Solar radiation Space exploration
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation's attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach's prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy.
AbstractList	Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation’s attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach’s prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy.
Author	Chiranjeevi, Madderla Moger, Tukaram Karlamangal, Skandha Jena, Debashisha
Author_xml	– sequence: 1 givenname: Madderla orcidid: 0009-0002-7837-796X surname: Chiranjeevi fullname: Chiranjeevi, Madderla email: chirumadderla.197ee012@nitk.edu.in organization: Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Mangalore, Surathkal, India – sequence: 2 givenname: Skandha surname: Karlamangal fullname: Karlamangal, Skandha organization: Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Mangalore, Surathkal, India – sequence: 3 givenname: Tukaram orcidid: 0000-0003-4176-5125 surname: Moger fullname: Moger, Tukaram organization: Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Mangalore, Surathkal, India – sequence: 4 givenname: Debashisha surname: Jena fullname: Jena, Debashisha organization: Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Mangalore, Surathkal, India
BookMark	eNp9kVFv0zAQxy00JEbZJ4CHSDyn2Lk4iR-7aGOVikC0PFsX-1Jcsrg4ydD49LjNkCYe8IvP5_v9dXf_1-yi9z0x9lbwpRBcfVjV9c12u8x4BksA4FkGL9hlJgqVgoTi4ln8il0Nw4HHU8WULC-Z3_oOQ7IOAa3D0fk--RLIOnMO7x6b4GxyG_CefvnwI_k2uH6ffKX9FCn3m2xS-_7Bd9OpHLvk2m22u0_pNQ7xazWNnnrjLYVkdTwGj-b7G_ayxW6gq6d7wXa3N7v6Lt18_riuV5vU5FyNKZTSkMp4AXlVETeNrQyY08PmQrRFyZXIbS6pEKppSxQoVAtFa5vGclXCgq1nWevxoI_B3WN41B6dPid82GsMozMd6bZRnDdUgChVDkIhcklNBZy4xIrLqPV-1ooT_JxoGPXBTyFOO-isUrIqcxX7XDA1V5nghyFQq40bzwsdA7pOC65PbunZLX1ySz-5FVn4h_3b8f-pdzPliOgZAXE3QsIfXgKikA
CODEN	IAECCG
CitedBy_id	crossref_primary_10_1080_01430750_2024_2414924 crossref_primary_10_7717_peerj_cs_3026 crossref_primary_10_1016_j_ejrh_2025_102663 crossref_primary_10_1371_journal_pone_0320089 crossref_primary_10_3390_electronics14142779
Cites_doi	10.1016/j.solener.2017.09.068 10.1109/ACCESS.2019.2923905 10.1109/ICPES.2017.8387332 10.1016/j.egyr.2022.10.071 10.1016/j.renene.2021.02.161 10.1007/s10661-014-3733-6 10.1016/j.epsr.2021.107633 10.1109/ACCESS.2019.2923006 10.1016/j.apenergy.2019.113315 10.1109/TSTE.2018.2888548 10.1016/j.asoc.2019.04.016 10.1016/j.enconman.2018.06.021 10.1109/RESEM57584.2023.10236100 10.1016/j.rser.2018.12.055 10.1016/j.renene.2015.08.068 10.1016/j.renene.2020.10.126 10.1109/smc.2016.7844673 10.1016/j.renene.2015.02.061 10.1109/TSTE.2021.3130949 10.1016/j.enconman.2019.112254 10.3390/app8050689 10.1111/rssc.12455 10.1109/ICEPE57949.2023.10201489 10.1016/j.enconman.2018.02.087 10.1109/access.2021.3100105 10.1016/j.egypro.2017.12.126 10.1007/s13369-021-05669-6 10.1016/j.solener.2021.12.066 10.1016/j.vibspec.2017.10.006 10.1016/j.renene.2019.02.087 10.1016/j.enconman.2018.07.089 10.1287/mnsc.42.7.1082 10.1109/PTC.2019.8810672 10.1016/j.ymssp.2020.107398 10.1016/j.compeleceng.2023.108804 10.1016/j.energy.2019.04.127
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
DBID	97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA
DOI	10.1109/ACCESS.2023.3330223
DatabaseName	IEEE Xplore (IEEE) IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional
DatabaseTitleList	Materials Research Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2169-3536
EndPage	131375
ExternalDocumentID	oai_doaj_org_article_fb900be631794319aa05eb830e05a805 10_1109_ACCESS_2023_3330223 10309115
Genre	orig-research
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c409t-375ce92063488e0cbd8c3c3488d411f670914d45e619bf7a1a19f36fdbbd0973
IEDL.DBID	RIE
ISICitedReferencesCount	5
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001112737500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2169-3536
IngestDate	Tue Oct 14 19:09:34 EDT 2025 Sun Nov 30 04:50:26 EST 2025 Tue Nov 18 22:06:02 EST 2025 Sat Nov 29 06:25:13 EST 2025 Wed Aug 27 02:22:16 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by-nc-nd/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c409t-375ce92063488e0cbd8c3c3488d411f670914d45e619bf7a1a19f36fdbbd0973
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0009-0002-7837-796X 0000-0003-4176-5125
OpenAccessLink	https://ieeexplore.ieee.org/document/10309115
PQID	2895874906
PQPubID	4845423
PageCount	14
ParticipantIDs	crossref_citationtrail_10_1109_ACCESS_2023_3330223 proquest_journals_2895874906 ieee_primary_10309115 crossref_primary_10_1109_ACCESS_2023_3330223 doaj_primary_oai_doaj_org_article_fb900be631794319aa05eb830e05a805
PublicationCentury	2000
PublicationDate	20230000 2023-00-00 20230101 2023-01-01
PublicationDateYYYYMMDD	2023-01-01
PublicationDate_xml	– year: 2023 text: 20230000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE access
PublicationTitleAbbrev	Access
PublicationYear	2023
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref34 ref15 ref37 ref14 ref31 ref30 ref11 ref33 ref10 ref32 ref2 ref17 ref39 ref16 ref38 ref19 ref18 ref24 ref23 ref26 ref25 ref20 Ren (ref40) 2018; 35 ref22 ref21 (ref1) 2019 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5
References_xml	– ident: ref13 doi: 10.1016/j.solener.2017.09.068 – ident: ref24 doi: 10.1109/ACCESS.2019.2923905 – ident: ref14 doi: 10.1109/ICPES.2017.8387332 – ident: ref30 doi: 10.1016/j.egyr.2022.10.071 – ident: ref32 doi: 10.1016/j.renene.2021.02.161 – ident: ref11 doi: 10.1007/s10661-014-3733-6 – ident: ref3 doi: 10.1016/j.epsr.2021.107633 – ident: ref27 doi: 10.1109/ACCESS.2019.2923006 – ident: ref26 doi: 10.1016/j.apenergy.2019.113315 – ident: ref22 doi: 10.1109/TSTE.2018.2888548 – ident: ref18 doi: 10.1016/j.asoc.2019.04.016 – ident: ref20 doi: 10.1016/j.enconman.2018.06.021 – ident: ref37 doi: 10.1109/RESEM57584.2023.10236100 – ident: ref7 doi: 10.1016/j.rser.2018.12.055 – ident: ref6 doi: 10.1016/j.renene.2015.08.068 – ident: ref8 doi: 10.1016/j.renene.2020.10.126 – ident: ref28 doi: 10.1109/smc.2016.7844673 – ident: ref23 doi: 10.1016/j.renene.2015.02.061 – ident: ref39 doi: 10.1109/TSTE.2021.3130949 – volume-title: Administration, U. International Energy Outlook 2019 With Projections to 2050 year: 2019 ident: ref1 – ident: ref10 doi: 10.1016/j.enconman.2019.112254 – ident: ref25 doi: 10.3390/app8050689 – ident: ref9 doi: 10.1111/rssc.12455 – ident: ref29 doi: 10.1109/ICEPE57949.2023.10201489 – ident: ref17 doi: 10.1016/j.enconman.2018.02.087 – ident: ref31 doi: 10.1109/access.2021.3100105 – volume: 35 start-page: 44 issue: 4 year: 2018 ident: ref40 article-title: Stock index forecast based on regularized LSTM model publication-title: Comput. Appl. Softw. – ident: ref19 doi: 10.1016/j.egypro.2017.12.126 – ident: ref12 doi: 10.1007/s13369-021-05669-6 – ident: ref5 doi: 10.1016/j.solener.2021.12.066 – ident: ref38 doi: 10.1016/j.vibspec.2017.10.006 – ident: ref21 doi: 10.1016/j.renene.2019.02.087 – ident: ref2 doi: 10.1016/j.enconman.2018.07.089 – ident: ref16 doi: 10.1287/mnsc.42.7.1082 – ident: ref15 doi: 10.1109/PTC.2019.8810672 – ident: ref34 doi: 10.1016/j.ymssp.2020.107398 – ident: ref33 doi: 10.1016/j.compeleceng.2023.108804 – ident: ref4 doi: 10.1016/j.energy.2019.04.127
SSID	ssj0000816957
Score	2.3445337
Snippet	Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	131362
SubjectTerms	Artificial neural networks Autoencoder Autoregressive processes Benchmarks BiLSTM convolution neural network Convolutional neural networks Decoding Encoding Forecasting Irradiance Irradiation Long short term memory Mathematical models Model accuracy Performance measurement Predictive models Radiation effects Representations Solar power generation Solar radiation Space exploration
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3PS-QwFA4ie9CD6Ko4_lhy8Gg1mbRpc5wZdlBYRXQO3kJ-woDMSB0F96_f99KMjAjuxVsb0qZ5ecl7X5t-HyGnRslaQCAqfBCuKKNqisZWMPFisCp4J01gSWyivrlpHh7U7YrUF-4J6-iBO8NdRKsYs0GKRGXGlTGsCrYRLLDKNB17KWQ9K2AqrcENl6qqM80QZ-piMBpBj85RLfxcAIjv98WHUJQY-7PEyqd1OQWb8TbZylkiHXRPt0PWwuwn2VzhDtwl83sEpfSqbZFdAM1Lb1v86pIOL9_wTyw6Xm69omlrAL1LyvPt9G_wdDSfvWa_g6aG0z_3k-tiCEHN08HLYo4Elz60dJBJx_fIZPx7MrossnpC4QCzLWDlqFxQfUhBYI4G5qxvnHB44kvOI_K28dKXVQAIZWNtuOEqChm9tR45fPbJ-mw-CweESh-8hHsoJ21pmLAGchBTR1v7Opqy7JH-0o7aZWZxFLh41AlhMKU742s0vs7G75Gz94ueOmKNr6sPcYDeqyIrdioAX9HZV_T_fKVH9nB4V9oTYAQO5cfL8dZ5Cj9rQKJVU5eKycPvaPuIbGB_urc3x2R90b6EE_LDvS6mz-2v5L3_AHhp8Jg priority: 102 providerName: Directory of Open Access Journals
Title	Solar Irradiation Prediction Hybrid Framework Using Regularized Convolutional BiLSTM-Based Autoencoder Approach
URI	https://ieeexplore.ieee.org/document/10309115 https://www.proquest.com/docview/2895874906 https://doaj.org/article/fb900be631794319aa05eb830e05a805
Volume	11
WOSCitedRecordID	wos001112737500001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Na9swFBdt2WE77LOj2bqiQ491KkeWZR2T0NDBWkqbQ29CH08QGHFxkkJ32N8-PVkJHWODXYxsJEv2T09P70n6PUJOjaolj4qo8MBdUQXVFI0VUfACWAXe1QZYCjYhr6-b-3t1kw-rp7MwAJA2n8EQk2kt37dug66ycwyJFYVT7JN9KWV_WGvnUMEIEkrIzCxUMnU-nk7jRwwxQPiQR7t9NOK_aZ9E0p-jqvwxFCf9Mnvzny17S17niSQd98i_I3uwfE9ePaMX_EDaO7Rb6deuQwICRIDedLgwk5KXT3hYi862u7No2j1Ab1Nw-m7xAzydtsvH3DVjVZPFt7v5VTGJes_T8WbdIgemh46OMy_5IZnPLubTyyIHWChcNOvWcXARDtQozlKiGANz1jeOO7zxVVkGpHYrK18JiFaWDdKUplSB18Fb65Hm5yM5WLZLOCK09uDr-A7lalsZxq2J0xQjg5VeBlNVAzLa_nftMvk4xsD4rpMRwpTuwdIIls5gDcjZrtBDz73x7-wTBHSXFYmz04OIlM5yqINVjFmoeWLGK5UxTIBtOAMmTMPEgBwius_q64EdkONt_9BZylc6GquikZVi9ae_FPtMXmITe5_NMTlYdxv4Ql64x_Vi1Z0kB0C8Xv28OEmd-Rea6PEO
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bb9MwFLZgIAEPXIcobOAHHknnJHYSP7bVqk501cT6sDfLl2Op0tSgrJ0Evx4fx62GEJP25kR24uTz8fHx5fsI-aplVZfBEWUOSptxL5usMSIYngcjwdlKA4tiE_Vi0VxdyYt0WD2ehQGAuPkMhpiMa_mutVucKjtBSaxgnOIxeSI4L_L-uNZ-SgU1JKSoE7dQzuTJaDIJnzFEifBhGSL3oij_8j-Rpj_pqvzTGUcPM331wLq9Ji_TUJKOeuzfkEewfkte3CEYfEfaS4xc6VnXIQUBYkAvOlyaicnZLzyuRae7_Vk07h-gP6I8fbf6DY5O2vVtapzhVePV_HJ5no2D53N0tN20yILpoKOjxEx-SJbT0-VkliWJhcyGwG4TuhdhQRZhnBIMGZg1rrGlxQvH89wjuVvOHRcQ4izja53rXPqy8s4Yh0Q_78nBul3DB0IrB64Kz5C2Mlyz0ugwUNG1N7WrveZ8QIrdf1c20Y-jCsa1imEIk6oHSyFYKoE1IN_2hX727Bv3Zx8joPusSJ0dbwSkVLJE5Y1kzEBVRm68XGrNBJimZMCEbpgYkENE9877emAH5GjXPlSy8xsVwlXR1Fyy6uN_in0hz2bL87many2-fyLPsbr9DM4ROdh0WzgmT-3tZnXTfY6N-Q_q1fIv
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=Solar+Irradiation+Prediction+Hybrid+Framework+Using+Regularized+Convolutional+BiLSTM-Based+Autoencoder+Approach&rft.jtitle=IEEE+access&rft.au=Chiranjeevi%2C+Madderla&rft.au=Karlamangal%2C+Skandha&rft.au=Moger%2C+Tukaram&rft.au=Jena%2C+Debashisha&rft.date=2023&rft.pub=IEEE&rft.eissn=2169-3536&rft.volume=11&rft.spage=131362&rft.epage=131375&rft_id=info:doi/10.1109%2FACCESS.2023.3330223&rft.externalDocID=10309115
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon