{"id":"https://openalex.org/W4415744777","doi":"https://doi.org/10.1109/icees67011.2025.11213272","title":"Robust Prediction of Solar Global Tilted Irradiance Using Gradient Boosting and Kneedle-Based Data Splitting","display_name":"Robust Prediction of Solar Global Tilted Irradiance Using Gradient Boosting and Kneedle-Based Data Splitting","publication_year":2025,"publication_date":"2025-08-21","ids":{"openalex":"https://openalex.org/W4415744777","doi":"https://doi.org/10.1109/icees67011.2025.11213272"},"language":null,"primary_location":{"id":"doi:10.1109/icees67011.2025.11213272","doi":"https://doi.org/10.1109/icees67011.2025.11213272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icees67011.2025.11213272","pdf_url":null,"source":{"id":"https://openalex.org/S7407086565","display_name":"International Conference on Electrical Energy Systems (ICEES)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 11th International Conference on Electrical Energy Systems (ICEES)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5119008846","display_name":"Kanaka Raju Kalla","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405270565","display_name":"Aditya Institute of Technology and Management","ror":"https://ror.org/00y0mva98","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405270565"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kanaka Raju Kalla","raw_affiliation_strings":["Aditya Institute of Technology and Management,Department of EEE,Tekkali,Andhra Pradesh,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aditya Institute of Technology and Management,Department of EEE,Tekkali,Andhra Pradesh,India","institution_ids":["https://openalex.org/I4405270565"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bibuti Bhusan Rath","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405270565","display_name":"Aditya Institute of Technology and Management","ror":"https://ror.org/00y0mva98","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405270565"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bibuti Bhusan Rath","raw_affiliation_strings":["Aditya Institute of Technology and Management,Department of EEE,Tekkali,Andhra Pradesh,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aditya Institute of Technology and Management,Department of EEE,Tekkali,Andhra Pradesh,India","institution_ids":["https://openalex.org/I4405270565"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069794177","display_name":"N K Rayaguru","orcid":"https://orcid.org/0000-0002-5990-5301","observed_orcids":["https://orcid.org/0000-0002-5990-5301"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"N K Rayaguru","raw_affiliation_strings":["VELTECH DEEMED TO BE UNIVERSITY,Department of EEE,Chennai,Tamil Nadu,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VELTECH DEEMED TO BE UNIVERSITY,Department of EEE,Chennai,Tamil Nadu,India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031823416","display_name":"Ravi Kumar Bodda","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405270565","display_name":"Aditya Institute of Technology and Management","ror":"https://ror.org/00y0mva98","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405270565"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ravi Kumar Bodda","raw_affiliation_strings":["Aditya Institute of Technology and Management,Department of EEE,Tekkali,Andhra Pradesh,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aditya Institute of Technology and Management,Department of EEE,Tekkali,Andhra Pradesh,India","institution_ids":["https://openalex.org/I4405270565"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049564200","display_name":"Ashok Ganga","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405270565","display_name":"Aditya Institute of Technology and Management","ror":"https://ror.org/00y0mva98","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405270565"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ashok Ganga","raw_affiliation_strings":["Aditya Institute of Technology and Management,Department of EEE,Tekkali,Andhra Pradesh,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aditya Institute of Technology and Management,Department of EEE,Tekkali,Andhra Pradesh,India","institution_ids":["https://openalex.org/I4405270565"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069138002","display_name":"Pramod Kumar Gouda","orcid":"https://orcid.org/0000-0001-7165-3496","observed_orcids":["https://orcid.org/0000-0001-7165-3496"]},"institutions":[{"id":"https://openalex.org/I4405270565","display_name":"Aditya Institute of Technology and Management","ror":"https://ror.org/00y0mva98","country_code":"IN","type":"education","lineage":["https://openalex.org/I4405270565"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pramod Kumar Gouda","raw_affiliation_strings":["Aditya Institute of Technology and Management,Department of EEE,Tekkali,Andhra Pradesh,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aditya Institute of Technology and Management,Department of EEE,Tekkali,Andhra Pradesh,India","institution_ids":["https://openalex.org/I4405270565"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1176,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.88582745,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"103","last_page":"108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":1.0,"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":1.0,"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/T10905","display_name":"Solar Thermal and Photovoltaic Systems","score":0.0,"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/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.0,"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/solar-irradiance-forecasting","display_name":"solar irradiance forecasting","score":0.8330000042915344},{"id":"https://openalex.org/keywords/gradient-boosting-regression","display_name":"gradient boosting regression","score":0.8140000104904175},{"id":"https://openalex.org/keywords/learning-curve","display_name":"learning curve","score":0.6679999828338623},{"id":"https://openalex.org/keywords/model-stability","display_name":"model stability","score":0.8500000238418579},{"id":"https://openalex.org/keywords/photovoltaic-energy-yield","display_name":"photovoltaic energy yield","score":0.9409999847412109},{"id":"https://openalex.org/keywords/meteorological-inputs","display_name":"meteorological inputs","score":0.7120000123977661}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7483000159263611},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.7257999777793884},{"id":"https://openalex.org/C46423501","wikidata":"https://www.wikidata.org/wiki/Q830654","display_name":"Irradiance","level":2,"score":0.6437000036239624},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5246000289916992},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.505299985408783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4878000020980835},{"id":"https://openalex.org/C541104983","wikidata":"https://www.wikidata.org/wiki/Q40015","display_name":"Solar energy","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C9695528","wikidata":"https://www.wikidata.org/wiki/Q7556707","display_name":"Solar irradiance","level":2,"score":0.4560999870300293},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.40860000252723694},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39969998598098755},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3783999979496002},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.36959999799728394},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.3695000112056732},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3610000014305115},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3375999927520752},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33739998936653137},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C147947694","wikidata":"https://www.wikidata.org/wiki/Q837552","display_name":"Numerical weather prediction","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3018999993801117},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icees67011.2025.11213272","doi":"https://doi.org/10.1109/icees67011.2025.11213272","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icees67011.2025.11213272","pdf_url":null,"source":{"id":"https://openalex.org/S7407086565","display_name":"International Conference on Electrical Energy Systems (ICEES)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 11th International Conference on Electrical Energy Systems (ICEES)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9853000044822693,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"},{"score":0.821399986743927,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"},{"score":0.7207000255584717,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1678356000","https://openalex.org/W2097749765","https://openalex.org/W2608353762","https://openalex.org/W3000458478","https://openalex.org/W3004959911","https://openalex.org/W3042732012","https://openalex.org/W4320155437","https://openalex.org/W4392400256","https://openalex.org/W4396680509","https://openalex.org/W4399413936","https://openalex.org/W4400449053","https://openalex.org/W4404167898"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,102,169,191],"of":[2,68,152,173,188],"Global":[3],"Tilted":[4],"Irradiance":[5],"(GTI)":[6],"is":[7],"critical":[8],"for":[9,162],"optimizing":[10],"photovoltaic":[11],"(PV)":[12],"energy":[13,190],"yield":[14,100],"in":[15,167],"real-world":[16,186],"settings.":[17],"This":[18],"paper":[19],"presents":[20],"a":[21,51,78],"machine":[22],"learning":[23,71],"framework":[24],"based":[25],"on":[26,92],"Gradient":[27],"Boosting":[28],"Regression":[29],"to":[30,39],"predict":[31],"GTI":[32,94],"using":[33,55],"diverse":[34],"meteorological":[35],"inputs.":[36],"In":[37],"addition":[38],"evaluating":[40],"traditional":[41],"fixed-ratio":[42],"data":[43,95],"splits":[44],"(60\u201340,":[45],"70\u201330,":[46],"80\u201320,":[47],"90\u201310),":[48],"we":[49,76],"introduce":[50],"data-driven":[52,174],"partitioning":[53],"strategy":[54],"the":[56,61,66,69,107,123,127,150,153,160],"Kneedle":[57],"algorithm,":[58],"which":[59],"identifies":[60],"optimal":[62],"split":[63,109,125],"point":[64],"from":[65,133],"curvature":[67],"model\u2019s":[70],"curve.":[72],"To":[73],"assess":[74],"robustness,":[75],"propose":[77],"novel":[79],"Trainability":[80,141],"Score":[81,142],"that":[82,97],"quantifies":[83],"model":[84,182],"stability":[85,177],"across":[86,146],"various":[87],"train-test":[88],"configurations.":[89],"Experimental":[90],"results":[91],"high-resolution":[93],"demonstrate":[96],"all":[98,147],"configurations":[99],"high":[101],"accuracy":[103],"(R2>":[104],"0.996),":[105],"with":[106],"Kneedle-based":[108],"achieving":[110],"competitive":[111],"performance":[112],"(R2=":[113],"0.9971,":[114],"MSE":[115,129],"=":[116],"258.02)":[117],"while":[118],"ensuring":[119],"balanced":[120],"evaluation.":[121],"Though":[122],"90\u201310":[124],"reports":[126],"lowest":[128],"(134.19),":[130],"it":[131],"suffers":[132],"reduced":[134],"test":[135],"diversity":[136],"and":[137,176,184],"potential":[138],"overfitting.":[139],"The":[140,171],"remains":[143],"consistent":[144],"(0.9993)":[145],"experiments,":[148],"highlighting":[149],"reliability":[151],"gradient":[154],"boosting":[155],"model.":[156],"Our":[157],"findings":[158],"emphasize":[159],"need":[161],"principled,":[163],"reproducible":[164],"evaluation":[165],"strategies":[166],"solar":[168],"studies.":[170],"integration":[172],"splitting":[175],"metrics":[178],"enables":[179],"more":[180],"transparent":[181],"comparisons":[183],"supports":[185],"deployment":[187],"AI-driven":[189],"systems.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-10-07T09:51:55.459714","created_date":"2025-10-31T00:00:00"}