{"id":"https://openalex.org/W7168123771","doi":"https://doi.org/10.1142/s1469026826500355","title":"Dual Reservoir-based Deep Learning Architecture for High-Resolution Solar Irradiance Prediction","display_name":"Dual Reservoir-based Deep Learning Architecture for High-Resolution Solar Irradiance Prediction","publication_year":2026,"publication_date":"2026-07-13","ids":{"openalex":"https://openalex.org/W7168123771","doi":"https://doi.org/10.1142/s1469026826500355"},"language":"en","primary_location":{"id":"doi:10.1142/s1469026826500355","doi":"https://doi.org/10.1142/s1469026826500355","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s1469026826500355","pdf_url":null,"source":{"id":"https://openalex.org/S206936884","display_name":"International Journal of Computational Intelligence and Applications","issn_l":"1469-0268","issn":["1469-0268","1757-5885"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-c","cwts-core","dongbi-c","jufo-1","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310311754","host_organization_name":"Imperial College Press","host_organization_lineage":["https://openalex.org/P4310311754"],"host_organization_lineage_names":["Imperial College Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence and Applications","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5095087533","display_name":"Ibrahem Mohammed Madhat","orcid":"https://orcid.org/0009-0007-5341-3174","observed_orcids":["https://orcid.org/0009-0007-5341-3174"]},"institutions":[{"id":"https://openalex.org/I171256487","display_name":"University of Technology - Iraq","ror":"https://ror.org/01w1ehb86","country_code":"IQ","type":"education","lineage":["https://openalex.org/I171256487"]}],"countries":["IQ"],"is_corresponding":false,"raw_author_name":"Ibrahem Mohammed Madhat","raw_affiliation_strings":["Department of the Office Affairs, University of Technology, Baghdad, Iraq"],"raw_orcid":"https://orcid.org/0009-0007-5341-3174","affiliations":[{"raw_affiliation_string":"Department of the Office Affairs, University of Technology, Baghdad, Iraq","institution_ids":["https://openalex.org/I171256487"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140543137","display_name":"Omar Alhebashi","orcid":"https://orcid.org/0009-0002-9276-8027","observed_orcids":["https://orcid.org/0009-0002-9276-8027"]},"institutions":[{"id":"https://openalex.org/I171256487","display_name":"University of Technology - Iraq","ror":"https://ror.org/01w1ehb86","country_code":"IQ","type":"education","lineage":["https://openalex.org/I171256487"]}],"countries":["IQ"],"is_corresponding":false,"raw_author_name":"Omar Alhebashi","raw_affiliation_strings":["Department of the Office Affairs, University of Technology, Baghdad, Iraq"],"raw_orcid":"https://orcid.org/0009-0002-9276-8027","affiliations":[{"raw_affiliation_string":"Department of the Office Affairs, University of Technology, Baghdad, Iraq","institution_ids":["https://openalex.org/I171256487"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140564289","display_name":"Ahmed Azzawi","orcid":"https://orcid.org/0009-0006-4674-0647","observed_orcids":["https://orcid.org/0009-0006-4674-0647"]},"institutions":[{"id":"https://openalex.org/I171256487","display_name":"University of Technology - Iraq","ror":"https://ror.org/01w1ehb86","country_code":"IQ","type":"education","lineage":["https://openalex.org/I171256487"]}],"countries":["IQ"],"is_corresponding":false,"raw_author_name":"Ahmed Azzawi","raw_affiliation_strings":["Department of the Office Affairs, University of Technology, Baghdad, Iraq"],"raw_orcid":"https://orcid.org/0009-0006-4674-0647","affiliations":[{"raw_affiliation_string":"Department of the Office Affairs, University of Technology, Baghdad, Iraq","institution_ids":["https://openalex.org/I171256487"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045125867","display_name":"Farhan Mohamed","orcid":"https://orcid.org/0000-0002-5298-8642","observed_orcids":["https://orcid.org/0000-0002-5298-8642"]},"institutions":[{"id":"https://openalex.org/I4576418","display_name":"University of Technology Malaysia","ror":"https://ror.org/026w31v75","country_code":"MY","type":"education","lineage":["https://openalex.org/I4576418"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Farhan Mohamed","raw_affiliation_strings":["Department of Computer Science, University of Technology Malaysia Kuala Lumpur, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-5298-8642","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Technology Malaysia Kuala Lumpur, Malaysia","institution_ids":["https://openalex.org/I4576418"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077069116","display_name":"Aminu Da\u2019u","orcid":"https://orcid.org/0000-0001-6198-0805","observed_orcids":["https://orcid.org/0000-0001-6198-0805"]},"institutions":[{"id":"https://openalex.org/I4210147342","display_name":"Al-Qalam University Katsina","ror":"https://ror.org/04fmgc680","country_code":"NG","type":"education","lineage":["https://openalex.org/I4210147342"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Aminu Dau","raw_affiliation_strings":["Department of Computer Science, Alqalam University Katsina, Katsina, Nigeria"],"raw_orcid":"https://orcid.org/0000-0001-6198-0805","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Alqalam University Katsina, Katsina, Nigeria","institution_ids":["https://openalex.org/I4210147342"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/solar-irradiance-forecasting","display_name":"solar irradiance forecasting","score":0.9670000076293945},{"id":"https://openalex.org/keywords/reservoir-computing","display_name":"reservoir computing","score":0.8330000042915344},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8700000047683716},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"long short-term memory","score":0.9610000252723694},{"id":"https://openalex.org/keywords/hybrid-cnn-lstm-model","display_name":"CNN-LSTM hybrid model","score":0.6949999928474426},{"id":"https://openalex.org/keywords/short-term-forecasting","display_name":"short-term forecasting","score":0.5839999914169312},{"id":"https://openalex.org/keywords/high-resolution-forecasting","display_name":"high-resolution forecasting","score":0.6320000290870667},{"id":"https://openalex.org/keywords/photovoltaic-integration","display_name":"photovoltaic integration","score":0.8830000162124634},{"id":"https://openalex.org/keywords/nonlinear-time-series","display_name":"nonlinear time series","score":0.48399999737739563},{"id":"https://openalex.org/keywords/temporal-memory","display_name":"temporal memory","score":0.5260000228881836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7591000199317932},{"id":"https://openalex.org/C9695528","wikidata":"https://www.wikidata.org/wiki/Q7556707","display_name":"Solar irradiance","level":2,"score":0.7159000039100647},{"id":"https://openalex.org/C46423501","wikidata":"https://www.wikidata.org/wiki/Q830654","display_name":"Irradiance","level":2,"score":0.6514999866485596},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.6233000159263611},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5527999997138977},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4902999997138977},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4726000130176544},{"id":"https://openalex.org/C541104983","wikidata":"https://www.wikidata.org/wiki/Q40015","display_name":"Solar energy","level":2,"score":0.445499986410141},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.3952000141143799},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.34279999136924744},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3368000090122223},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3312999904155731},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3231000006198883},{"id":"https://openalex.org/C2777618391","wikidata":"https://www.wikidata.org/wiki/Q1483757","display_name":"Solar power","level":3,"score":0.3172999918460846},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.30880001187324524},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C423512","wikidata":"https://www.wikidata.org/wiki/Q383973","display_name":"Electricity generation","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s1469026826500355","doi":"https://doi.org/10.1142/s1469026826500355","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s1469026826500355","pdf_url":null,"source":{"id":"https://openalex.org/S206936884","display_name":"International Journal of Computational Intelligence and Applications","issn_l":"1469-0268","issn":["1469-0268","1757-5885"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["ccf-c","cwts-core","dongbi-c","jufo-1","norway-1","poland-20","russia-white-list-2"],"host_organization":"https://openalex.org/P4310311754","host_organization_name":"Imperial College Press","host_organization_lineage":["https://openalex.org/P4310311754"],"host_organization_lineage_names":["Imperial College Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9853000044822693},{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.9786999821662903},{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4943999946117401}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W3037246536","https://openalex.org/W3190154316","https://openalex.org/W4213438055","https://openalex.org/W4239510810","https://openalex.org/W4293531551","https://openalex.org/W4300009529","https://openalex.org/W4306408437","https://openalex.org/W4316466886","https://openalex.org/W4327604033","https://openalex.org/W4382721968","https://openalex.org/W4384816741","https://openalex.org/W4386141843","https://openalex.org/W4386291530","https://openalex.org/W4387149480","https://openalex.org/W4390591073","https://openalex.org/W4391847860","https://openalex.org/W4396732637","https://openalex.org/W4402229242","https://openalex.org/W4402633011","https://openalex.org/W4403545034","https://openalex.org/W4404539180","https://openalex.org/W4405751310","https://openalex.org/W4405980342","https://openalex.org/W4406237323","https://openalex.org/W4406729139","https://openalex.org/W4406917696","https://openalex.org/W4408159887","https://openalex.org/W4408789980","https://openalex.org/W4408953978","https://openalex.org/W4412420231","https://openalex.org/W4412778588","https://openalex.org/W4413794989","https://openalex.org/W4414437447"],"related_works":["https://openalex.org/W2947749663","https://openalex.org/W3118278143","https://openalex.org/W3097099747","https://openalex.org/W7220197832","https://openalex.org/W4407873585","https://openalex.org/W4414183230","https://openalex.org/W6893610895","https://openalex.org/W4377702035","https://openalex.org/W4417169148","https://openalex.org/W4308216925"],"abstract_inverted_index":{"Accurate":[0],"short-term":[1],"solar":[2,22,64,94],"irradiance":[3,23,65,95],"forecasting":[4,122,230],"is":[5,90,139],"a":[6,49,253,260],"critical":[7],"enabler":[8],"for":[9,62],"Photovoltaic":[10],"(PV)":[11],"power":[12,19],"integration,":[13],"and":[14,27,80,83,108,124,133,145,169,179,200,209],"energy":[15],"management":[16],"in":[17,44],"renewable-dominated":[18],"systems.":[20],"Nevertheless,":[21],"exhibits":[24],"strong":[25],"nonlinearity,":[26],"temporal":[28,85],"variability,":[29],"which":[30],"significantly":[31],"limit":[32],"the":[33,101,109,146,155,158,185,189,221,227,232,235,249],"performance":[34,138],"of":[35,100,148,165,195],"conventional":[36],"models.":[37,219],"To":[38],"address":[39],"these":[40],"challenges":[41],"among":[42],"others,":[43],"this":[45],"study":[46],"we":[47],"propose":[48],"Dual":[50],"Reservoir-based":[51],"Convolutional":[52],"Neural":[53],"Network":[54],"Integrated":[55],"with":[56,154,184],"Long":[57],"Short-Term":[58],"Memory":[59],"(DR\u2013CNN\u2013LSTM)":[60],"architecture":[61,224],"high-resolution":[63],"prediction.":[66],"The":[67,87],"proposed":[68,88,159,190,222],"framework":[69,160],"introduces":[70],"two":[71],"parallel":[72],"reservoir":[73,251],"modules":[74],"to":[75],"enrich":[76],"nonlinear":[77],"state":[78],"representations":[79],"enhance":[81],"short-":[82],"mid-term":[84],"memory.":[86],"model":[89,191],"validated":[91],"using":[92,141,206],"multi-year":[93],"data":[96],"from":[97],"multiple":[98,121],"stations":[99],"California":[102],"Irrigation":[103],"Management":[104],"Information":[105],"System":[106],"(CIMIS)":[107],"Solar":[110],"Radiation":[111],"Research":[112],"Laboratory":[113],"(SRRL)":[114],"dataset.":[115],"Extensive":[116],"experimentation":[117],"was":[118,175,239],"conducted":[119],"across":[120],"horizons":[123],"benchmarked":[125],"against":[126],"state-of-the-art":[127,186],"models,":[128],"including":[129],"LSTM,":[130],"CNN\u2013LSTM,":[131],"CNN\u2013DeepESN":[132,187],"single-reservoir":[134],"hybrid":[135],"architectures.":[136],"Forecasting":[137],"evaluated":[140],"MAE,":[142],"RMSE,":[143],"MAPE,":[144],"coefficient":[147],"determination":[149],"([Formula:":[150],"see":[151,245],"text]).":[152],"Compared":[153],"CNN\u2013LSTM":[156],"baseline,":[157],"achieved":[161,192,226],"average":[162],"RMSE":[163,193],"reductions":[164,194],"10.7%":[166],"on":[167,171,198,202],"CIMIS":[168,199],"11.5%":[170],"SRRL,":[172],"while":[173],"MAE":[174],"reduced":[176],"by":[177],"14.0%":[178],"15.1%,":[180],"respectively.":[181],"Furthermore,":[182],"compared":[183],"model,":[188],"approximately":[196],"5.2%":[197],"5.5%":[201],"SRRL.":[203],"Statistical":[204],"analyses":[205],"Friedman,":[207],"Nemenyi,":[208],"Wilcoxon":[210],"signed-rank":[211],"tests":[212],"confirmed":[213],"significant":[214,242],"improvements":[215],"over":[216,234],"most":[217],"baseline":[218],"Although":[220],"dual-reservoir":[223],"consistently":[225],"best":[228],"overall":[229],"performance,":[231],"improvement":[233],"Single":[236],"Reservoir\u2013CNN\u2013LSTM":[237],"variant":[238],"not":[240],"statistically":[241,261],"at":[243],"[Formula:":[244],"text],":[246],"indicating":[247],"that":[248],"second":[250],"provides":[252],"moderate":[254],"but":[255],"consistent":[256],"enhancement":[257],"rather":[258],"than":[259],"dominant":[262],"gain.":[263]},"counts_by_year":[],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2026-07-14T00:00:00"}