{"id":"https://openalex.org/W4400770853","doi":"https://doi.org/10.1109/access.2024.3430385","title":"Off-Grid Small-Scale Power Forecasting Using Optimized Machine Learning Algorithms","display_name":"Off-Grid Small-Scale Power Forecasting Using Optimized Machine Learning Algorithms","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4400770853","doi":"https://doi.org/10.1109/access.2024.3430385"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3430385","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3430385","pdf_url":null,"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":null,"license_id":null,"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://doi.org/10.1109/access.2024.3430385","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039333139","display_name":"Aadyasha Patel","orcid":"https://orcid.org/0000-0002-9903-9794"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Aadyasha Patel","raw_affiliation_strings":["School of Electrical Engineering, Vellore Institute of Technology, Chennai, India"],"raw_orcid":"https://orcid.org/0000-0002-9903-9794","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Vellore Institute of Technology, Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109605528","display_name":"O.V. Gnana Swathika","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"O. V. Gnana Swathika","raw_affiliation_strings":["Centre for Smart Grid Technologies, School of Electrical Engineering, Vellore Institute of Technology, Chennai, India"],"raw_orcid":"https://orcid.org/0000-0003-1238-7671","affiliations":[{"raw_affiliation_string":"Centre for Smart Grid Technologies, School of Electrical Engineering, Vellore Institute of Technology, Chennai, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039333139"],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.9868,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88163586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"107406","last_page":"107419"},"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":0.9991999864578247,"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":0.9988999962806702,"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/photovoltaic-system","display_name":"Photovoltaic system","score":0.7754831314086914},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6589111089706421},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5901761651039124},{"id":"https://openalex.org/keywords/renewable-energy","display_name":"Renewable energy","score":0.5272409319877625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5026407241821289},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.45392271876335144},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4265305995941162},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4258185625076294},{"id":"https://openalex.org/keywords/solar-energy","display_name":"Solar energy","score":0.41080060601234436},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.22624295949935913},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1677982211112976},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.09385493397712708}],"concepts":[{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.7754831314086914},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6589111089706421},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5901761651039124},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.5272409319877625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5026407241821289},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.45392271876335144},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4265305995941162},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4258185625076294},{"id":"https://openalex.org/C541104983","wikidata":"https://www.wikidata.org/wiki/Q40015","display_name":"Solar energy","level":2,"score":0.41080060601234436},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.22624295949935913},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1677982211112976},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.09385493397712708},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3430385","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3430385","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4ffcb342f8f74903b73a35241a8a185c","is_oa":true,"landing_page_url":"https://doaj.org/article/4ffcb342f8f74903b73a35241a8a185c","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":"IEEE Access, Vol 12, Pp 107406-107419 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3430385","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3430385","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1969911362","https://openalex.org/W2049807778","https://openalex.org/W2127170577","https://openalex.org/W2147331493","https://openalex.org/W2161110389","https://openalex.org/W2492975200","https://openalex.org/W2621900912","https://openalex.org/W2806583606","https://openalex.org/W3186247783","https://openalex.org/W4206101647","https://openalex.org/W4211129303","https://openalex.org/W4220726206","https://openalex.org/W4285198798","https://openalex.org/W4285384244","https://openalex.org/W4288901802","https://openalex.org/W4320168785","https://openalex.org/W4366966733","https://openalex.org/W4367333149"],"related_works":["https://openalex.org/W2386968573","https://openalex.org/W2395064349","https://openalex.org/W2034374297","https://openalex.org/W2090763504","https://openalex.org/W2382628689","https://openalex.org/W2766130412","https://openalex.org/W2526730640","https://openalex.org/W148178222","https://openalex.org/W2351171996","https://openalex.org/W2983370139"],"abstract_inverted_index":{"Solar":[0],"energy":[1,7,62,118],"is":[2,27,90,127,153,167],"highly":[3],"unstable.":[4],"Total":[5],"photovoltaic":[6,25,37,97,123],"generated":[8],"varies":[9],"based":[10],"on":[11,54,79],"changes":[12],"in":[13],"weather,":[14],"climate":[15],"and":[16,45,104,148,176,194],"seasons.":[17],"Owing":[18],"to":[19,29,34,51,59,91,155,201],"the":[20,55,71,75,81,93,116,130,157],"arbitrariness":[21],"of":[22,67,87,101,106,132,143,185],"solar":[23,61,109],"energy,":[24],"output":[26,95],"prone":[28],"frequent":[30],"power":[31,38,68],"oscillations.":[32],"Due":[33],"this":[35,48,88],"reason,":[36],"prediction":[39,126],"becomes":[40],"obligatory.":[41],"Through":[42],"comprehensive":[43],"analysis":[44,166],"critical":[46],"examination,":[47],"research":[49],"endeavours":[50],"shed":[52],"light":[53],"pursuit":[56],"with":[57,74],"regard":[58],"embracing":[60],"as":[63],"a":[64],"sustainable":[65],"channel":[66],"generation.":[69],"Additionally,":[70],"study":[72,89],"aligns":[73],"United":[76],"Nations\u2019":[77],"commitment":[78],"achieving":[80],"Sustainable":[82],"Development":[83],"Goals.":[84],"The":[85,165,178],"objective":[86],"prognosticate":[92],"electricity":[94],"from":[96,120],"panel":[98],"through":[99,129],"compilation":[100],"meteorological":[102],"parameters":[103],"computation":[105],"hourly":[107],"global":[108],"radiation":[110],"upon":[111],"an":[112,121],"inclined":[113,122],"surface,":[114],"alongside":[115],"resultant":[117],"yield":[119],"panel.":[124],"This":[125],"executed":[128],"deployment":[131],"four":[133],"optimized":[134,183],"machine":[135,158],"learning":[136,159],"methods,":[137],"specifically":[138],"Support":[139],"Vector":[140],"Machines,":[141],"Ensemble":[142,184],"Trees,":[144],"Gaussian":[145],"Process":[146],"Regression":[147],"Neural":[149],"Networks.":[150],"Bayesian":[151],"Optimization":[152],"utilized":[154],"optimize":[156],"models":[160],"by":[161],"tuning":[162],"its":[163,202],"hyperparameters.":[164],"conducted":[168],"across":[169,190],"three":[170],"distinct":[171],"dataset":[172,192],"classifications:":[173],"annual,":[174],"monthly":[175],"seasonal.":[177],"empirical":[179],"findings":[180],"underscore":[181],"that":[182],"Trees":[186],"exhibits":[187],"superior":[188],"performance":[189],"all":[191],"classifications":[193],"also":[195],"necessitates":[196],"shortest":[197],"training":[198],"duration":[199],"compared":[200],"counterparts.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}