{"id":"https://openalex.org/W4414293308","doi":"https://doi.org/10.32604/cmc.2025.066888","title":"Solar Radiation Prediction Using Boosted Coyote Optimization Algorithm with Deep Learning for Energy Management","display_name":"Solar Radiation Prediction Using Boosted Coyote Optimization Algorithm with Deep Learning for Energy Management","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414293308","doi":"https://doi.org/10.32604/cmc.2025.066888"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.066888","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066888","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.066888","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077354305","display_name":"Shekaina Justin","orcid":"https://orcid.org/0009-0009-4966-7096"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shekaina Justin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103203875","display_name":"Wafaa Saleh","orcid":"https://orcid.org/0000-0001-6552-2318"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wafaa Saleh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064596547","display_name":"Hind Albalawi","orcid":"https://orcid.org/0000-0003-1506-1175"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hind Mohammed Albalawi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5024402908","display_name":"J Shermina","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"J. Shermina","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5077354305"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13753944,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"85","issue":"3","first_page":"5469","last_page":"5487"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9589999914169312,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9589999914169312,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9146999716758728,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6305000185966492},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5781999826431274},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5562999844551086},{"id":"https://openalex.org/keywords/solar-irradiance","display_name":"Solar irradiance","score":0.546500027179718},{"id":"https://openalex.org/keywords/solar-energy","display_name":"Solar energy","score":0.5180000066757202},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47909998893737793},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.3774000108242035}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6305000185966492},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5781999826431274},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5562999844551086},{"id":"https://openalex.org/C9695528","wikidata":"https://www.wikidata.org/wiki/Q7556707","display_name":"Solar irradiance","level":2,"score":0.546500027179718},{"id":"https://openalex.org/C541104983","wikidata":"https://www.wikidata.org/wiki/Q40015","display_name":"Solar energy","level":2,"score":0.5180000066757202},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47909998893737793},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4657999873161316},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.45010000467300415},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3783999979496002},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3441999852657318},{"id":"https://openalex.org/C153385146","wikidata":"https://www.wikidata.org/wiki/Q18335","display_name":"Radiation","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C147947694","wikidata":"https://www.wikidata.org/wiki/Q837552","display_name":"Numerical weather prediction","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.328000009059906},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31150001287460327},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C168754636","wikidata":"https://www.wikidata.org/wiki/Q620920","display_name":"Climate model","level":3,"score":0.2825999855995178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27869999408721924},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.066888","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066888","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.066888","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066888","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2904672395","https://openalex.org/W2963928450","https://openalex.org/W3097850930","https://openalex.org/W3212644427","https://openalex.org/W4200565213","https://openalex.org/W4206133833","https://openalex.org/W4210605514","https://openalex.org/W4210620976","https://openalex.org/W4210818944","https://openalex.org/W4223977378","https://openalex.org/W4225139506","https://openalex.org/W4292379757","https://openalex.org/W4307029612","https://openalex.org/W4309323351","https://openalex.org/W4362511711","https://openalex.org/W4385470738","https://openalex.org/W4392574239","https://openalex.org/W4392859425","https://openalex.org/W4392922618","https://openalex.org/W4394704443","https://openalex.org/W4394894147","https://openalex.org/W4396912628","https://openalex.org/W4399055407","https://openalex.org/W4399861765","https://openalex.org/W4406289400","https://openalex.org/W4408953978","https://openalex.org/W4408954614"],"related_works":[],"abstract_inverted_index":{"Solar":[0,69,100,159],"radiation":[1,20,31,101,139,211,236],"is":[2,33,71,92,216,232,257,261,275],"the":[3,16,42,55,72,81,85,108,178,188,195,223,229,243,282],"main":[4],"source":[5],"of":[6,34,57,75,84,110,228,291],"energy":[7,59,70],"on":[8],"Earth":[9],"and":[10,23,26,41,50,78,97,127,136,147,281],"plays":[11],"a":[12,62,66,157,164,182,199,288],"major":[13],"role":[14],"in":[15,151],"hydrological":[17],"cycles,":[18],"surface":[19],"balance,":[21],"weather":[22],"climate":[24,39],"changes,":[25],"vegetation":[27],"photosynthesis.":[28],"Accurate":[29],"solar":[30,43,58,129,138,210,235],"prediction":[32,46,102],"paramount":[35],"importance":[36],"for":[37,94,207],"both":[38],"research":[40],"industry.":[44],"This":[45,154],"includes":[47,107],"forecasting":[48,88,209],"techniques":[49],"advanced":[51],"modeling":[52],"to":[53,80,125,186,266,278],"evaluate":[54],"amount":[56],"available":[60],"at":[61],"specific":[63],"location":[64],"during":[65],"given":[67],"period.":[68],"cheapest":[73],"form":[74],"clean":[76],"energy,":[77,86],"due":[79],"intermittent":[82],"nature":[83,190],"accurate":[87,152],"across":[89],"multiple":[90],"timeframes":[91],"necessary":[93],"efficient":[95],"generation":[96],"demand":[98],"management.":[99],"using":[103,181,222,234],"deep":[104],"learning":[105],"(DL)":[106],"applications":[109],"neural":[111],"network":[112],"methods,":[113],"namely":[114],"Convolutional":[115],"Neural":[116],"Network":[117],"(CNN)":[118],"or":[119],"Long":[120,201],"Short-Term":[121,202],"Memory":[122,203],"(LSTM)":[123],"models,":[124],"forecast":[126],"model":[128,162,175],"irradiance":[130],"patterns.":[131],"By":[132],"leveraging":[133],"meteorological":[134],"variables":[135],"historical":[137],"data,":[140],"DL":[141],"algorithms":[142],"can":[143],"capture":[144],"complex":[145],"spatial":[146],"temporal":[148],"dependencies,":[149],"resulting":[150],"predictions.":[153],"article":[155],"presents":[156],"novel":[158],"Radiation":[160],"Prediction":[161],"utilizing":[163],"Boosted":[165],"Coyote":[166],"Optimization":[167],"Algorithm":[168],"with":[169],"Deep":[170,200],"Learning":[171],"(SRP-BCOADL).":[172],"The":[173,213,225,252,269],"SRP-BCOADL":[174,196,230,244],"initially":[176],"normalizes":[177],"input":[179],"data":[180],"min-max":[183],"normalization":[184],"approach":[185],"improve":[187],"robust":[189],"under":[191],"different":[192],"scales.":[193],"Besides,":[194],"technique":[197,231],"uses":[198],"Autoencoder":[204],"(DLSTM-AE)":[205],"system":[206],"precisely":[208],"levels.":[212],"model\u2019s":[214],"accuracy":[215],"further":[217],"improved":[218],"through":[219],"hyperparameter":[220],"optimization":[221],"BCOA.":[224],"performance":[226],"analysis":[227],"tested":[233],"data.":[237],"Extensive":[238],"experimental":[239],"outcomes":[240],"prove":[241],"that":[242],"method":[245],"obtains":[246],"better":[247],"results":[248],"over":[249],"other":[250,267],"techniques.":[251],"Mean":[253,271,283],"Squared":[254,272],"Error":[255,273,285],"(MSE)":[256],"just":[258],"0.13":[259],"kWh/m2,":[260,280],"much":[262],"lower":[263],"when":[264],"compared":[265],"models.":[268],"Root":[270],"(RMSE)":[274],"also":[276],"reduced":[277],"0.36":[279],"Absolute":[284],"(MAE)":[286],"reaches":[287],"minimal":[289],"level":[290],"0.276":[292],"kWh/m2.":[293]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
