{"id":"https://openalex.org/W7124454117","doi":"https://doi.org/10.23919/aise.2025.000020","title":"Exploring the Interpretability of Forecasting Models for Energy Balancing Market","display_name":"Exploring the Interpretability of Forecasting Models for Energy Balancing Market","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W7124454117","doi":"https://doi.org/10.23919/aise.2025.000020"},"language":null,"primary_location":{"id":"doi:10.23919/aise.2025.000020","doi":"https://doi.org/10.23919/aise.2025.000020","is_oa":false,"landing_page_url":"https://doi.org/10.23919/aise.2025.000020","pdf_url":null,"source":{"id":"https://openalex.org/S5406978643","display_name":"Artificial Intelligence Science and Engineering","issn_l":"2097-5104","issn":["2097-5104"],"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Science and Engineering","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2602.00049","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123243655","display_name":"Oskar V\u00e5le","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I184942183","display_name":"University of Oslo","ror":"https://ror.org/01xtthb56","country_code":"NO","type":"education","lineage":["https://openalex.org/I184942183"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Oskar V\u00e5le","raw_affiliation_strings":["University of Oslo,Department of Informatics,Oslo,Norway,0316"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oslo,Department of Informatics,Oslo,Norway,0316","institution_ids":["https://openalex.org/I184942183"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124050428","display_name":"SHILIANG ZHANG","orcid":"https://orcid.org/0000-0002-9524-1602","observed_orcids":["https://orcid.org/0000-0002-9524-1602"]},"institutions":[{"id":"https://openalex.org/I184942183","display_name":"University of Oslo","ror":"https://ror.org/01xtthb56","country_code":"NO","type":"education","lineage":["https://openalex.org/I184942183"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Shiliang Zhang","raw_affiliation_strings":["University of Oslo,Department of Informatics,Oslo,Norway,0316"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oslo,Department of Informatics,Oslo,Norway,0316","institution_ids":["https://openalex.org/I184942183"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056311070","display_name":"Sabita Maharjan","orcid":"https://orcid.org/0000-0002-4616-8488","observed_orcids":["https://orcid.org/0000-0002-4616-8488"]},"institutions":[{"id":"https://openalex.org/I184942183","display_name":"University of Oslo","ror":"https://ror.org/01xtthb56","country_code":"NO","type":"education","lineage":["https://openalex.org/I184942183"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Sabita Maharjan","raw_affiliation_strings":["University of Oslo,Department of Informatics,Oslo,Norway,0316"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Oslo,Department of Informatics,Oslo,Norway,0316","institution_ids":["https://openalex.org/I184942183"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084464577","display_name":"Gro Kl\u00e6boe","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I204778367","display_name":"Norwegian University of Science and Technology","ror":"https://ror.org/05xg72x27","country_code":"NO","type":"education","lineage":["https://openalex.org/I204778367"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Gro Kl\u00e6boe","raw_affiliation_strings":["Norwegian University of Science and Technology,Department of Electric Energy,Trondheim,Norway,7034"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Norwegian University of Science and Technology,Department of Electric Energy,Trondheim,Norway,7034","institution_ids":["https://openalex.org/I204778367"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.99,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.92272055,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"1","issue":"4","first_page":"295","last_page":"306"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.41769999265670776,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.41769999265670776,"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":0.32499998807907104,"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/T10424","display_name":"Electric Power System Optimization","score":0.15710000693798065,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/explainable-boosting-machine","display_name":"explainable boosting machine","score":0.9290000200271606},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.9559999704360962},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.9380000233650208},{"id":"https://openalex.org/keywords/black-box-modeling","display_name":"black-box models","score":0.7170000076293945},{"id":"https://openalex.org/keywords/accuracy-interpretability-trade-off","display_name":"accuracy-interpretability trade-off","score":0.8309999704360962}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9710999727249146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6166999936103821},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5864999890327454},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.574400007724762},{"id":"https://openalex.org/C2776849302","wikidata":"https://www.wikidata.org/wiki/Q1239166","display_name":"Energy market","level":3,"score":0.5728999972343445},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5708000063896179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5525000095367432},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.546999990940094},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C146733006","wikidata":"https://www.wikidata.org/wiki/Q676081","display_name":"Electricity market","level":3,"score":0.3564999997615814},{"id":"https://openalex.org/C2781104810","wikidata":"https://www.wikidata.org/wiki/Q23580049","display_name":"Electricity price forecasting","level":4,"score":0.35530000925064087},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3336000144481659},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.32899999618530273},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2840000092983246},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C2776190866","wikidata":"https://www.wikidata.org/wiki/Q1341477","display_name":"Energy supply","level":3,"score":0.2660999894142151}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.23919/aise.2025.000020","doi":"https://doi.org/10.23919/aise.2025.000020","is_oa":false,"landing_page_url":"https://doi.org/10.23919/aise.2025.000020","pdf_url":null,"source":{"id":"https://openalex.org/S5406978643","display_name":"Artificial Intelligence Science and Engineering","issn_l":"2097-5104","issn":["2097-5104"],"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Science and Engineering","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2602.00049","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2602.00049","pdf_url":"https://arxiv.org/pdf/2602.00049","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2602.00049","doi":"https://doi.org/10.48550/arxiv.2602.00049","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.00049","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2602.00049","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2602.00049","pdf_url":"https://arxiv.org/pdf/2602.00049","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.8485000133514404,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"sustainable_development_goals_aurora":[{"score":0.775420069694519,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2295598076","https://openalex.org/W2787894218","https://openalex.org/W3131884169","https://openalex.org/W4382935439","https://openalex.org/W4387835345","https://openalex.org/W4388334816","https://openalex.org/W4400148306","https://openalex.org/W4401357523","https://openalex.org/W4401871219","https://openalex.org/W4402273187","https://openalex.org/W4404037131","https://openalex.org/W4406983075","https://openalex.org/W4407597792","https://openalex.org/W4407937756","https://openalex.org/W4408101702","https://openalex.org/W4409485041","https://openalex.org/W4410965369","https://openalex.org/W4411014918","https://openalex.org/W4411336947","https://openalex.org/W4411403206"],"related_works":[],"abstract_inverted_index":{"The":[0],"balancing":[1,15,24,72,90,218],"market":[2,25,91,94,194],"in":[3,11,22,88,213],"the":[4,16,23,54,62,70,77,89,103,124,131,160,165,169,172,178,214,217],"energy":[5,38,71,98],"sector":[6],"plays":[7],"a":[8,134,151,202],"critical":[9],"role":[10],"physically":[12],"and":[13,18,30,36,67,116,127,192,204],"financially":[14],"supply":[17],"demand.":[19],"Modeling":[20],"dynamics":[21],"can":[26,45],"provide":[27],"valuable":[28,205],"insights":[29,186],"prognosis":[31],"for":[32,69,168,216],"power":[33],"grid":[34],"stability":[35],"secure":[37],"supply.":[39],"While":[40],"complex":[41,209],"machine":[42,115,119],"learning":[43],"models":[44,132,212],"achieve":[46],"high":[47],"accuracy,":[48],"their":[49],"\u201cblack-box\u201d":[50],"nature":[51],"severely":[52],"limits":[53],"model":[55,65],"interpretability.":[56,155],"In":[57],"this":[58],"paper,":[59],"we":[60,75,128],"explore":[61,102],"trade-off":[63],"between":[64],"accuracy":[66,145],"interpretability":[68,104,183],"market.":[73,219],"Particularly,":[74],"take":[76],"example":[78],"of":[79,105,154,162],"forecasting":[80,107,144],"manual":[81],"frequency":[82],"restoration":[83],"reserve":[84],"(mFRR)":[85],"activation":[86,173],"price":[87,99,167,174,190],"using":[92,108],"real":[93],"data":[95],"from":[96,177],"different":[97],"zones.":[100],"We":[101,121],"mFRR":[106,166,189],"two":[109,125],"models:":[110],"extreme":[111],"gradient":[112],"boosting":[113,118],"(XGBoost)":[114],"explainable":[117],"(EBM).":[120],"also":[122,158],"integrate":[123],"models,":[126],"benchmark":[129],"all":[130],"against":[133],"baseline":[135],"naive":[136],"model.":[137],"Our":[138,156,196],"results":[139],"show":[140],"that":[141,199],"EBM":[142,200],"provides":[143],"comparable":[146],"to":[147,208],"XGBoost":[148],"while":[149],"yielding":[150],"considerable":[152],"level":[153],"analysis":[157],"underscores":[159],"challenge":[161],"accurately":[163],"predicting":[164],"instances":[170],"when":[171],"deviates":[175],"significantly":[176],"spot":[179],"price.":[180],"Importantly,":[181],"EBM's":[182],"features":[184],"reveal":[185],"into":[187],"non-linear":[188],"drivers":[191],"regional":[193],"dynamics.":[195],"study":[197],"demonstrates":[198],"is":[201],"viable":[203],"interpretable":[206],"alternative":[207],"black-box":[210],"AI":[211],"forecast":[215]},"counts_by_year":[{"year":2026,"cited_by_count":7}],"updated_date":"2026-09-29T14:09:16.559479","created_date":"2026-01-17T00:00:00"}