{"id":"https://openalex.org/W7126053755","doi":"https://doi.org/10.1016/j.iswa.2026.200630","title":"WOA-FCM-CNN-WNN-informer: An advanced hybrid deep learning model for ultra-accurate PV power forecasting in electric mobility","display_name":"WOA-FCM-CNN-WNN-informer: An advanced hybrid deep learning model for ultra-accurate PV power forecasting in electric mobility","publication_year":2026,"publication_date":"2026-01-29","ids":{"openalex":"https://openalex.org/W7126053755","doi":"https://doi.org/10.1016/j.iswa.2026.200630"},"language":"en","primary_location":{"id":"doi:10.1016/j.iswa.2026.200630","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.iswa.2026.200630","pdf_url":null,"source":{"id":"https://openalex.org/S4210234522","display_name":"Intelligent Systems with Applications","issn_l":"2667-3053","issn":["2667-3053"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Systems with Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.iswa.2026.200630","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5099218511","display_name":"Lazhar Manai","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lazhar Manai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093504326","display_name":"Walid Mchara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Walid Mchara","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124191200","display_name":"Mohamed Abdellatif Khalfa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohamed Abdellatif Khalfa","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061222881","display_name":"Monia Raissi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Monia Raissi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5119273776","display_name":"Wissem Dimassi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wissem Dimassi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5099218511"],"corresponding_institution_ids":[],"apc_list":{"value":1500,"currency":"USD","value_usd":1500},"apc_paid":{"value":1500,"currency":"USD","value_usd":1500},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30512498,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":null,"first_page":"200630","last_page":"200630"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.4564000070095062,"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"}},"topics":[{"id":"https://openalex.org/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.4564000070095062,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.3037000000476837,"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/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.07649999856948853,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5472999811172485},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.5300999879837036},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5289000272750854},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5019000172615051},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44909998774528503},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4348999857902527},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.42089998722076416},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.38920000195503235},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.3887999951839447},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.3801000118255615}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6671000123023987},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5472999811172485},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.5300999879837036},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5289000272750854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5264999866485596},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5019000172615051},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44909998774528503},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4302999973297119},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.42089998722076416},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.3801000118255615},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3540000021457672},{"id":"https://openalex.org/C77715397","wikidata":"https://www.wikidata.org/wiki/Q931447","display_name":"Electrical load","level":3,"score":0.3522000014781952},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3370000123977661},{"id":"https://openalex.org/C10558101","wikidata":"https://www.wikidata.org/wiki/Q689855","display_name":"Smart grid","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.31850001215934753},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.296999990940094},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C423512","wikidata":"https://www.wikidata.org/wiki/Q383973","display_name":"Electricity generation","level":3,"score":0.28690001368522644},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.2777000069618225},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C29470771","wikidata":"https://www.wikidata.org/wiki/Q4165150","display_name":"Neuro-fuzzy","level":4,"score":0.27630001306533813},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.2750000059604645},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C195975749","wikidata":"https://www.wikidata.org/wiki/Q1475705","display_name":"Fuzzy control system","level":3,"score":0.26019999384880066},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C186108316","wikidata":"https://www.wikidata.org/wiki/Q352530","display_name":"Adaptive neuro fuzzy inference system","level":4,"score":0.2547999918460846}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.iswa.2026.200630","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.iswa.2026.200630","pdf_url":null,"source":{"id":"https://openalex.org/S4210234522","display_name":"Intelligent Systems with Applications","issn_l":"2667-3053","issn":["2667-3053"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Systems with Applications","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:40b3eeb095114a57bbb88f201b046ba1","is_oa":true,"landing_page_url":"https://doaj.org/article/40b3eeb095114a57bbb88f201b046ba1","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Intelligent Systems with Applications, Vol 29, Iss , Pp 200630- (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1016/j.iswa.2026.200630","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.iswa.2026.200630","pdf_url":null,"source":{"id":"https://openalex.org/S4210234522","display_name":"Intelligent Systems with Applications","issn_l":"2667-3053","issn":["2667-3053"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Systems with Applications","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7739342451095581,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1208518215","https://openalex.org/W1979221753","https://openalex.org/W2061438946","https://openalex.org/W2096684483","https://openalex.org/W2123235979","https://openalex.org/W2278589437","https://openalex.org/W2764205829","https://openalex.org/W2792326773","https://openalex.org/W2794078445","https://openalex.org/W2796179209","https://openalex.org/W2801761896","https://openalex.org/W2802229284","https://openalex.org/W2811192320","https://openalex.org/W3007191831","https://openalex.org/W3033131237","https://openalex.org/W3089230449","https://openalex.org/W3092177662","https://openalex.org/W3110443213","https://openalex.org/W3133901049","https://openalex.org/W3134625449","https://openalex.org/W3183572711","https://openalex.org/W3190326505","https://openalex.org/W3212721454","https://openalex.org/W4221032146","https://openalex.org/W4226345300","https://openalex.org/W4283723885","https://openalex.org/W4285254505","https://openalex.org/W4295832542","https://openalex.org/W4306871700","https://openalex.org/W4376607167","https://openalex.org/W4385665723","https://openalex.org/W4386434846","https://openalex.org/W4388017346","https://openalex.org/W4388341011","https://openalex.org/W4389779731","https://openalex.org/W4391502258","https://openalex.org/W4393406494","https://openalex.org/W4394896902","https://openalex.org/W4394911329","https://openalex.org/W4394911439","https://openalex.org/W4396674761","https://openalex.org/W4399768753","https://openalex.org/W4400647947","https://openalex.org/W4401155067","https://openalex.org/W4402699280","https://openalex.org/W4402702622","https://openalex.org/W4402900247","https://openalex.org/W4403422068","https://openalex.org/W4405440718","https://openalex.org/W4405440846","https://openalex.org/W4405743873","https://openalex.org/W4407168670","https://openalex.org/W4407367946","https://openalex.org/W4407414357","https://openalex.org/W4409150905","https://openalex.org/W4410476890","https://openalex.org/W4410814653","https://openalex.org/W4413121263"],"related_works":[],"abstract_inverted_index":{"Effective":[0],"prediction":[1,50],"of":[2,129,181],"photovoltaic":[3],"(PV)":[4],"power":[5,133],"generation":[6],"is":[7,104,123],"essential":[8],"for":[9,61,106,117,197],"enhancing":[10],"energy":[11,199],"management":[12],"in":[13,139,209],"solar-powered":[14],"electric":[15],"vehicles.":[16],"This":[17,52],"study":[18],"introduces":[19,54],"an":[20],"innovative":[21],"hybrid":[22],"forecasting":[23],"framework":[24,156],"that":[25],"combines":[26],"Fuzzy":[27],"C-Means":[28],"(FCM)":[29],"clustering,":[30],"Convolutional":[31],"Neural":[32,36],"Networks":[33,37],"(CNN),":[34],"Wavelet":[35],"(WNN),":[38],"the":[39,43,191],"Informer":[40],"architecture,":[41],"and":[42,110,153,168,194,204],"Whale":[44,91],"Optimization":[45,92],"Algorithm":[46,93],"(WOA)":[47,94],"to":[48,146],"improve":[49],"accuracy.":[51],"approach":[53],"a":[55,81,85,100,126,178],"condition-aware,":[56],"end-to-end":[57],"FCM-CNN-WNN-Informer":[58],"pipeline":[59],"tailored":[60],"PV":[62,132],"dynamics,":[63],"where:":[64],"(i)":[65],"similar-day":[66],"fuzzy":[67],"clustering":[68],"normalizes":[69],"weather":[70],"heterogeneity":[71],"before":[72],"learning;":[73],"(ii)":[74],"wavelet-based":[75],"multi-scale":[76],"features":[77],"are":[78],"injected":[79],"into":[80],"long-horizon":[82],"Informer;":[83],"(iii)":[84],"global,":[86],"cross-module":[87],"hyperparameter":[88],"search":[89],"via":[90],"jointly":[95],"tunes":[96],"all":[97],"stages;":[98],"(iv)":[99],"Generalization":[101],"Index":[102],"(GI)":[103],"proposed":[105,120],"robust":[107],"model":[108,122],"selection;":[109],"(v)":[111],"Monte-Carlo":[112],"dropout":[113],"quantifies":[114],"predictive":[115],"uncertainty":[116],"practical":[118],"deployment.The":[119],"WOA-FCM-CNN-WNN-Informer":[121],"evaluated":[124],"on":[125],"comprehensive":[127],"dataset":[128],"70,080":[130],"hourly":[131],"recordings":[134],"gathered":[135],"over":[136],"eight":[137],"years":[138],"Tunisia.":[140],"Results":[141],"show":[142],"superior":[143],"performance":[144],"compared":[145],"standard":[147],"deep":[148],"learning":[149],"models":[150],"like":[151],"LSTM":[152],"BiLSTM.":[154],"The":[155],"reduces":[157],"Mean":[158,170],"Absolute":[159],"Percentage":[160],"Error":[161,172],"(MAPE)":[162],"by":[163,174],"as":[164,166],"much":[165],"98.52%":[167],"Root":[169],"Squared":[171],"(RMSE)":[173],"93.84%,":[175],"while":[176],"maintaining":[177],"high":[179],"coefficient":[180],"determination":[182],"(R2=0.98)":[183],"across":[184],"varying":[185],"meteorological":[186],"conditions.":[187],"These":[188],"outcomes":[189],"underscore":[190],"model\u2019s":[192],"robustness":[193],"its":[195],"promise":[196],"advancing":[198],"utilization,":[200],"refining":[201],"charging":[202],"strategies,":[203],"supporting":[205],"intelligent":[206],"route":[207],"planning":[208],"solar-electric":[210],"transportation":[211],"systems.":[212]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2026-01-30T00:00:00"}