{"id":"https://openalex.org/W4388739481","doi":"https://doi.org/10.1109/access.2023.3333895","title":"A Multistage Hybrid Deep Learning Model for Enhanced Solar Tracking","display_name":"A Multistage Hybrid Deep Learning Model for Enhanced Solar Tracking","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4388739481","doi":"https://doi.org/10.1109/access.2023.3333895"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3333895","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3333895","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10320347.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10320347.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082808405","display_name":"Mwenge Mulenga","orcid":"https://orcid.org/0000-0001-5961-4830"},"institutions":[{"id":"https://openalex.org/I4210160828","display_name":"National Institute of Public Administration","ror":"https://ror.org/057943037","country_code":"ZM","type":"education","lineage":["https://openalex.org/I4210160828"]}],"countries":["ZM"],"is_corresponding":true,"raw_author_name":"Mwenge Mulenga","raw_affiliation_strings":["Business Studies Division, National Institute of Public Administration, Lusaka, Zambia"],"raw_orcid":"https://orcid.org/0000-0001-5961-4830","affiliations":[{"raw_affiliation_string":"Business Studies Division, National Institute of Public Administration, Lusaka, Zambia","institution_ids":["https://openalex.org/I4210160828"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075480641","display_name":"Musa Phiri","orcid":"https://orcid.org/0000-0003-1171-8336"},"institutions":[{"id":"https://openalex.org/I3132976927","display_name":"Mulungushi University","ror":"https://ror.org/02vmcxs72","country_code":"ZM","type":"education","lineage":["https://openalex.org/I3132976927"]}],"countries":["ZM"],"is_corresponding":false,"raw_author_name":"Musa Phiri","raw_affiliation_strings":["School of Engineering and Technology, Mulungushi University, Kabwe, Zambia"],"raw_orcid":"https://orcid.org/0000-0003-1171-8336","affiliations":[{"raw_affiliation_string":"School of Engineering and Technology, Mulungushi University, Kabwe, Zambia","institution_ids":["https://openalex.org/I3132976927"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000948813","display_name":"Luckson Simukonda","orcid":null},"institutions":[{"id":"https://openalex.org/I3132976927","display_name":"Mulungushi University","ror":"https://ror.org/02vmcxs72","country_code":"ZM","type":"education","lineage":["https://openalex.org/I3132976927"]}],"countries":["ZM"],"is_corresponding":false,"raw_author_name":"Luckson Simukonda","raw_affiliation_strings":["School of Engineering and Technology, Mulungushi University, Kabwe, Zambia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and Technology, Mulungushi University, Kabwe, Zambia","institution_ids":["https://openalex.org/I3132976927"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041831427","display_name":"Fadele Ayotunde Alaba","orcid":"https://orcid.org/0000-0002-5842-1452"},"institutions":[{"id":"https://openalex.org/I4210097403","display_name":"Federal College of Education, Kano","ror":"https://ror.org/00wqp8t15","country_code":"NG","type":"education","lineage":["https://openalex.org/I4210097403"]},{"id":"https://openalex.org/I4402554091","display_name":"Federal College of Education, Zaria","ror":"https://ror.org/03g5rm013","country_code":null,"type":"education","lineage":["https://openalex.org/I4402554091"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Fadele A. Alaba","raw_affiliation_strings":["Department of Computer Science, Federal College of Education, Zaria, Kaduna, Nigeria","Department of Computer Science, Federal College of Education, Zaria, Kaduna State, Nigeria"],"raw_orcid":"https://orcid.org/0000-0002-5842-1452","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal College of Education, Zaria, Kaduna, Nigeria","institution_ids":["https://openalex.org/I4210097403"]},{"raw_affiliation_string":"Department of Computer Science, Federal College of Education, Zaria, Kaduna State, Nigeria","institution_ids":["https://openalex.org/I4210097403","https://openalex.org/I4402554091"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5082808405"],"corresponding_institution_ids":["https://openalex.org/I4210160828"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.5166,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.86402472,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"129449","last_page":"129466"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9988999962806702,"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/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.9916999936103821,"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/computer-science","display_name":"Computer science","score":0.7462015151977539},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5403606295585632},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47887295484542847},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.45708465576171875},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4427200257778168},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41047564148902893}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7462015151977539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5403606295585632},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47887295484542847},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.45708465576171875},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4427200257778168},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41047564148902893},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3333895","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3333895","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10320347.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:15ad2bf9db6147c5895566c9fadf9baf","is_oa":true,"landing_page_url":"https://doaj.org/article/15ad2bf9db6147c5895566c9fadf9baf","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 129449-129466 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3333895","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3333895","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10320347.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388739481.pdf","grobid_xml":"https://content.openalex.org/works/W4388739481.grobid-xml"},"referenced_works_count":73,"referenced_works":["https://openalex.org/W1965217321","https://openalex.org/W1975362087","https://openalex.org/W2034027084","https://openalex.org/W2064900402","https://openalex.org/W2101713460","https://openalex.org/W2102148524","https://openalex.org/W2511683089","https://openalex.org/W2521002152","https://openalex.org/W2543678400","https://openalex.org/W2580598803","https://openalex.org/W2584491527","https://openalex.org/W2598379669","https://openalex.org/W2738093346","https://openalex.org/W2765335521","https://openalex.org/W2770058012","https://openalex.org/W2792244305","https://openalex.org/W2807567209","https://openalex.org/W2903265999","https://openalex.org/W2904803257","https://openalex.org/W2905181083","https://openalex.org/W2905388713","https://openalex.org/W2913872388","https://openalex.org/W2915806950","https://openalex.org/W2920009758","https://openalex.org/W2926822960","https://openalex.org/W2942586591","https://openalex.org/W2945020349","https://openalex.org/W2963928450","https://openalex.org/W2970021049","https://openalex.org/W2981648891","https://openalex.org/W3001672905","https://openalex.org/W3012897071","https://openalex.org/W3016415013","https://openalex.org/W3036363354","https://openalex.org/W3044163491","https://openalex.org/W3046298529","https://openalex.org/W3081707209","https://openalex.org/W3097203985","https://openalex.org/W3102910059","https://openalex.org/W3107299738","https://openalex.org/W3111518852","https://openalex.org/W3112217566","https://openalex.org/W3114013176","https://openalex.org/W3120463026","https://openalex.org/W3133311089","https://openalex.org/W3135523675","https://openalex.org/W3167881307","https://openalex.org/W3178107640","https://openalex.org/W3195403325","https://openalex.org/W3214840804","https://openalex.org/W3216316500","https://openalex.org/W3217247703","https://openalex.org/W4200550314","https://openalex.org/W4206796315","https://openalex.org/W4220686422","https://openalex.org/W4226148019","https://openalex.org/W4229658977","https://openalex.org/W4297984414","https://openalex.org/W4307154428","https://openalex.org/W4312369753","https://openalex.org/W4315752282","https://openalex.org/W4316466886","https://openalex.org/W4361792570","https://openalex.org/W4362473002","https://openalex.org/W4366826269","https://openalex.org/W4378574043","https://openalex.org/W4378576185","https://openalex.org/W4381663834","https://openalex.org/W4385975731","https://openalex.org/W6675357634","https://openalex.org/W6757207288","https://openalex.org/W6797538778","https://openalex.org/W6800962201"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W2939353110","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756"],"abstract_inverted_index":{"Solar":[0],"tracking":[1,94],"helps":[2],"maximize":[3],"the":[4,16,45,58,97,104,121,134,143,166,200,203],"efficiency":[5],"of":[6,49,113,165,170,202,231],"solar":[7,13,30,37,93,208],"applications,":[8],"such":[9],"as":[10],"photovoltaic":[11],"(PV)":[12],"panels.":[14],"In":[15,118],"recent":[17],"past,":[18],"machine":[19],"learning":[20,88],"(ML)":[21],"techniques":[22],"have":[23],"been":[24],"extensively":[25],"used":[26],"to":[27,44,55,70,74,91,116,129,141,145,154,162],"implement":[28],"automatic":[29],"tracking.":[31,209],"However,":[32],"applying":[33],"predictive":[34,64],"models":[35,65],"in":[36,103,133],"trackers":[38],"is":[39],"a":[40,67,85,119,146,171,218],"non-trivial":[41],"task":[42],"due":[43],"randomness":[46],"and":[47,111,125,137,149,184,192,228,234],"non-linearity":[48],"meteorological":[50,75],"data,":[51,76,135],"limiting":[52,78],"their":[53,79],"ability":[54],"clearly":[56],"represent":[57],"underlying":[59],"data":[60,105,115,139,144,157],"patterns.":[61],"Most":[62],"existing":[63,215],"take":[66,163],"monolithic":[68],"approach":[69],"addressing":[71],"limitations":[72],"related":[73],"thereby":[77],"performance.":[80,95],"Therefore,":[81],"this":[82],"paper":[83],"proposes":[84],"deep":[86],"hybrid":[87,212],"(DHL)":[89],"model":[90,99,122,177,213],"enhance":[92],"Furthermore,":[96],"proposed":[98,211],"improves":[100],"feature":[101,167,194],"representation":[102,161],"by":[106],"using":[107],"combined":[108],"normalization":[109],"methods":[110,205,216],"conversion":[112],"numerical":[114],"images.":[117],"nutshell,":[120],"integrates":[123],"sine":[124],"cosine":[126],"transformations":[127,140],"(SCT)":[128],"reveal":[130],"cyclical":[131],"patterns":[132],"sigmoid":[136],"minimum-maximum":[138],"scale":[142],"Gaussian":[147],"distribution,":[148],"Gramian":[150],"Angular":[151],"Fields":[152],"(GAF)":[153],"convert":[155],"tabular":[156],"into":[158],"2D":[159],"image":[160],"advantage":[164],"extraction":[168],"capability":[169],"convolutional":[172],"neural":[173],"network":[174],"(CNN).":[175],"The":[176,196,210],"also":[178],"utilizes":[179],"long-term":[180],"short-term":[181],"memory":[182],"(LSTM)":[183],"gated":[185],"recurrent":[186],"units":[187],"(GRU)":[188],"for":[189],"both":[190],"spatial":[191],"temporal":[193],"extraction.":[195],"results":[197],"show":[198],"that":[199],"aggregation":[201],"above-mentioned":[204],"significantly":[206],"enhances":[207],"outperforms":[214],"on":[217],"publicly":[219],"available":[220],"dataset,":[221],"achieving":[222],"outstanding":[223],"performance":[224],"with":[225],"MAE,":[226],"MAPE,":[227],"RMSE":[229],"scores":[230],"0.0073,":[232],"1.4635,":[233],"0.0097,":[235],"respectively.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}