{"id":"https://openalex.org/W7225826185","doi":"https://doi.org/10.3390/app162211175","title":"Explainable Artificial Intelligence for Three-Hour-Ahead Natural Gas Consumption Forecasting with Uncertainty Quantification","display_name":"Explainable Artificial Intelligence for Three-Hour-Ahead Natural Gas Consumption Forecasting with Uncertainty Quantification","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225826185","doi":"https://doi.org/10.3390/app162211175"},"language":"en","primary_location":{"id":"doi:10.3390/app162211175","doi":"https://doi.org/10.3390/app162211175","is_oa":true,"landing_page_url":"https://doi.org/10.3390/app162211175","pdf_url":null,"source":{"id":"https://openalex.org/S4210205812","display_name":"Applied Sciences","issn_l":"2076-3417","issn":["2076-3417"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","oarl","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Sciences","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/app162211175","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092599173","display_name":"Dmytro Tymoshchuk","orcid":"https://orcid.org/0000-0003-0246-2236","observed_orcids":["https://orcid.org/0000-0003-0246-2236"]},"institutions":[{"id":"https://openalex.org/I4210147056","display_name":"Ternopil Ivan Pului National Technical University","ror":"https://ror.org/045hmbf04","country_code":"UA","type":"education","lineage":["https://openalex.org/I4210147056"]}],"countries":["UA"],"is_corresponding":true,"raw_author_name":"Dmytro Tymoshchuk","raw_affiliation_strings":["Department of Artificial Intelligence Systems and Data Analysis, Ternopil Ivan Puluj National Technical University, 46001 Ternopil, Ukraine"],"raw_orcid":"https://orcid.org/0000-0003-0246-2236","affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence Systems and Data Analysis, Ternopil Ivan Puluj National Technical University, 46001 Ternopil, Ukraine","institution_ids":["https://openalex.org/I4210147056"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086358203","display_name":"Grigorii Shymchuk","orcid":"https://orcid.org/0000-0003-2362-7386","observed_orcids":["https://orcid.org/0000-0003-2362-7386"]},"institutions":[{"id":"https://openalex.org/I4210147056","display_name":"Ternopil Ivan Pului National Technical University","ror":"https://ror.org/045hmbf04","country_code":"UA","type":"education","lineage":["https://openalex.org/I4210147056"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Grigorii Shymchuk","raw_affiliation_strings":["Department of Computer Science, Ternopil Ivan Puluj National Technical University, 46001 Ternopil, Ukraine"],"raw_orcid":"https://orcid.org/0000-0003-2362-7386","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Ternopil Ivan Puluj National Technical University, 46001 Ternopil, Ukraine","institution_ids":["https://openalex.org/I4210147056"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032119981","display_name":"Iaroslav Lytvynenko","orcid":"https://orcid.org/0000-0001-7311-4103","observed_orcids":["https://orcid.org/0000-0001-7311-4103"]},"institutions":[{"id":"https://openalex.org/I4210147056","display_name":"Ternopil Ivan Pului National Technical University","ror":"https://ror.org/045hmbf04","country_code":"UA","type":"education","lineage":["https://openalex.org/I4210147056"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Iaroslav Lytvynenko","raw_affiliation_strings":["Department of Computer Science, Ternopil Ivan Puluj National Technical University, 46001 Ternopil, Ukraine"],"raw_orcid":"https://orcid.org/0000-0001-7311-4103","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Ternopil Ivan Puluj National Technical University, 46001 Ternopil, Ukraine","institution_ids":["https://openalex.org/I4210147056"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048307830","display_name":"Serhii A. Lupenko","orcid":"https://orcid.org/0000-0002-6559-0721","observed_orcids":["https://orcid.org/0000-0002-6559-0721"]},"institutions":[{"id":"https://openalex.org/I119217307","display_name":"Opole University of Technology","ror":"https://ror.org/05sj5k538","country_code":"PL","type":"education","lineage":["https://openalex.org/I119217307"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Serhii Lupenko","raw_affiliation_strings":["Faculty of Computer Science, Opole University of Technology, Pr\u00f3szkowska 76, 45-758 Opole, Poland","Institute of Telecommunications and Global Information Space, 02000 Kyiv, Ukraine"],"raw_orcid":"https://orcid.org/0000-0002-6559-0721","affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, Opole University of Technology, Pr\u00f3szkowska 76, 45-758 Opole, Poland","institution_ids":["https://openalex.org/I119217307"]},{"raw_affiliation_string":"Institute of Telecommunications and Global Information Space, 02000 Kyiv, Ukraine","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030939421","display_name":"Mariusz Rz\u0105sa","orcid":"https://orcid.org/0000-0002-3461-2131","observed_orcids":["https://orcid.org/0000-0002-3461-2131"]},"institutions":[{"id":"https://openalex.org/I119217307","display_name":"Opole University of Technology","ror":"https://ror.org/05sj5k538","country_code":"PL","type":"education","lineage":["https://openalex.org/I119217307"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Mariusz Rz\u0105sa","raw_affiliation_strings":["Faculty of Computer Science, Opole University of Technology, Pr\u00f3szkowska 76, 45-758 Opole, Poland"],"raw_orcid":"https://orcid.org/0000-0002-3461-2131","affiliations":[{"raw_affiliation_string":"Faculty of Computer Science, Opole University of Technology, Pr\u00f3szkowska 76, 45-758 Opole, Poland","institution_ids":["https://openalex.org/I119217307"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5048307830","https://openalex.org/A5092599173"],"corresponding_institution_ids":["https://openalex.org/I119217307","https://openalex.org/I4210147056"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2896},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2896},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":"22","first_page":"11175","last_page":"11175"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9970999956130981,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9970999956130981,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7228999733924866},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5684999823570251},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.5475999712944031},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5412999987602234},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44940000772476196},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43470001220703125},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.3855000138282776},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.382099986076355},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.35690000653266907},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C120934525","wikidata":"https://www.wikidata.org/wiki/Q849149","display_name":"Absolute deviation","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3422999978065491},{"id":"https://openalex.org/C2992367939","wikidata":"https://www.wikidata.org/wiki/Q4121144","display_name":"Gas consumption","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C71907059","wikidata":"https://www.wikidata.org/wiki/Q223323","display_name":"Root mean square","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C105054674","wikidata":"https://www.wikidata.org/wiki/Q3707381","display_name":"Mean difference","level":3,"score":0.31690001487731934},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.305400013923645},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3010999858379364},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.27250000834465027},{"id":"https://openalex.org/C42683663","wikidata":"https://www.wikidata.org/wiki/Q3517834","display_name":"Mean radiant temperature","level":3,"score":0.25949999690055847},{"id":"https://openalex.org/C59427239","wikidata":"https://www.wikidata.org/wiki/Q40858","display_name":"Natural gas","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/app162211175","doi":"https://doi.org/10.3390/app162211175","is_oa":true,"landing_page_url":"https://doi.org/10.3390/app162211175","pdf_url":null,"source":{"id":"https://openalex.org/S4210205812","display_name":"Applied Sciences","issn_l":"2076-3417","issn":["2076-3417"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","oarl","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Sciences","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/app162211175","doi":"https://doi.org/10.3390/app162211175","is_oa":true,"landing_page_url":"https://doi.org/10.3390/app162211175","pdf_url":null,"source":{"id":"https://openalex.org/S4210205812","display_name":"Applied Sciences","issn_l":"2076-3417","issn":["2076-3417"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","oarl","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Sciences","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1985258161","https://openalex.org/W2042506099","https://openalex.org/W2047694274","https://openalex.org/W2093230975","https://openalex.org/W2117178635","https://openalex.org/W2162174678","https://openalex.org/W2295598076","https://openalex.org/W2799430435","https://openalex.org/W2911964244","https://openalex.org/W2949676527","https://openalex.org/W2964060211","https://openalex.org/W2999615587","https://openalex.org/W4200551830","https://openalex.org/W4221072577","https://openalex.org/W4225342234","https://openalex.org/W4283796987","https://openalex.org/W4291327732","https://openalex.org/W4304127761","https://openalex.org/W4308929518","https://openalex.org/W4309432772","https://openalex.org/W4323034150","https://openalex.org/W4389512180","https://openalex.org/W4392401475","https://openalex.org/W4394964798","https://openalex.org/W4401814071","https://openalex.org/W4402134419","https://openalex.org/W4402830563","https://openalex.org/W4410317361","https://openalex.org/W4410346879","https://openalex.org/W4413779113","https://openalex.org/W4414453774"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"investigates":[2],"short-term":[3],"natural":[4,24],"gas":[5,25,145,184],"consumption":[6,26,146,170,185,211],"forecasting":[7,21,243],"in":[8,212],"the":[9,14,91,104,117,135,152,179,189,193,209,228,233],"city":[10,247],"of":[11,16,38,98,113,214,222],"Ternopil":[12],"and":[13,27,42,55,80,107,128,248],"contribution":[15],"different":[17],"feature":[18,165],"groups":[19],"to":[20,32,148,163,242],"accuracy.":[22],"Synchronized":[23],"meteorological":[28,161],"observations":[29],"from":[30,87],"2007":[31],"2019":[33],"were":[34,45,60,63],"used.":[35],"A":[36],"total":[37],"203":[39],"calendar,":[40],"meteorological,":[41],"derived":[43],"features":[44,147,150,162,180],"constructed.":[46],"Six":[47],"machine":[48],"learning":[49],"models,":[50],"three":[51],"naive":[52],"baseline":[53,136],"forecasts,":[54],"a":[56,108,164,218,245],"seasonal":[57],"ETS":[58],"model":[59,67],"compared.":[61],"Hyperparameters":[62],"optimized":[64],"using":[65,71],"Optuna,":[66],"performance":[68],"was":[69,83,124],"evaluated":[70,181],"six":[72],"temporal":[73],"folds":[74,106],"with":[75,217],"an":[76],"expanding":[77],"training":[78],"window,":[79],"final":[81],"testing":[82],"conducted":[84],"on":[85,116],"data":[86],"2019.":[88],"XGBoost":[89,127],"achieved":[90],"lowest":[92],"mean":[93,95,110,153,155,195,230],"root":[94,109,154],"squared":[96,111,156],"error":[97,112,157,175],"4307.13":[99],"\u00b1":[100],"280.29":[101],"m3":[102,115],"across":[103],"cross-validation":[105],"6517.29":[114],"test":[118,250],"set.":[119],"No":[120],"statistically":[121],"significant":[122],"difference":[123],"found":[125],"between":[126],"Random":[129],"Forest,":[130],"although":[131],"both":[132],"models":[133],"outperformed":[134],"forecasts.":[137],"Feature":[138],"ablation":[139],"analysis":[140],"showed":[141],"that":[142,167,207],"adding":[143],"historical":[144],"calendar":[149],"reduced":[151,173],"by":[158,176],"81.42%.":[159],"Adding":[160],"set":[166],"already":[168],"included":[169],"history":[171],"further":[172],"this":[174],"0.51%.":[177],"Among":[178],"for":[182,244],"XGBoost,":[183],"24":[186],"h":[187],"before":[188],"target":[190],"interval":[191,235],"had":[192,227],"highest":[194],"absolute":[196],"SHAP":[197],"value.":[198],"The":[199,238],"adaptive":[200],"online":[201],"conformal":[202],"method":[203],"produced":[204],"prediction":[205,234],"intervals":[206,226],"contained":[208],"observed":[210],"89.32%":[213],"cases,":[215],"compared":[216],"nominal":[219],"coverage":[220],"level":[221],"90%.":[223],"However,":[224],"these":[225],"largest":[229],"width":[231],"among":[232],"methods":[236],"considered.":[237],"findings":[239],"are":[240],"limited":[241],"single":[246],"one":[249],"year.":[251]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
