{"id":"https://openalex.org/W3211411902","doi":"https://doi.org/10.1109/iecon48115.2021.9589097","title":"Probabilistic DAM price forecasting using a combined Quantile Regression Deep Neural Network with less-crossing quantiles","display_name":"Probabilistic DAM price forecasting using a combined Quantile Regression Deep Neural Network with less-crossing quantiles","publication_year":2021,"publication_date":"2021-10-13","ids":{"openalex":"https://openalex.org/W3211411902","doi":"https://doi.org/10.1109/iecon48115.2021.9589097","mag":"3211411902"},"language":"en","primary_location":{"id":"doi:10.1109/iecon48115.2021.9589097","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon48115.2021.9589097","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2021 \u2013 47th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"},"type":"article","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/A5071722799","display_name":"Ties van der Heijden","orcid":null},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Ties van der Heijden","raw_affiliation_strings":["Electrical Sustainable Energy, Water Resources Engineering, Delft University of Technology, Delft, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Electrical Sustainable Energy, Water Resources Engineering, Delft University of Technology, Delft, The Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022327673","display_name":"Peter P\u00e1lensk\u00fd","orcid":"https://orcid.org/0000-0003-3183-4705"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Peter Palensky","raw_affiliation_strings":["Electrical Sustainable Energy, Delft University of Technology, Delft, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Electrical Sustainable Energy, Delft University of Technology, Delft, The Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078479966","display_name":"Edo Abraham","orcid":"https://orcid.org/0000-0003-0989-5456"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Edo Abraham","raw_affiliation_strings":["Water Resources Engineering, Delft University of Technology, Delft, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Water Resources Engineering, Delft University of Technology, Delft, The Netherlands","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5071722799"],"corresponding_institution_ids":["https://openalex.org/I98358874"],"apc_list":null,"apc_paid":null,"fwci":0.5228,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69388265,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"221","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9984999895095825,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9980999827384949,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9977999925613403,"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/quantile","display_name":"Quantile","score":0.918198823928833},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.8505966663360596},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6871623396873474},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.615081787109375},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5929387211799622},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5815026760101318},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.557519257068634},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.5172213315963745},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42557796835899353},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.32668769359588623},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3194943070411682},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22877848148345947}],"concepts":[{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.918198823928833},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.8505966663360596},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6871623396873474},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.615081787109375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5929387211799622},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5815026760101318},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.557519257068634},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.5172213315963745},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42557796835899353},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32668769359588623},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3194943070411682},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22877848148345947}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iecon48115.2021.9589097","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon48115.2021.9589097","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2021 \u2013 47th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"},{"id":"pmh:oai:tudelft.nl:uuid:cbcc9c19-a70c-42dd-b671-a0bf53de474e","is_oa":false,"landing_page_url":"http://resolver.tudelft.nl/uuid:cbcc9c19-a70c-42dd-b671-a0bf53de474e","pdf_url":null,"source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2008709824","https://openalex.org/W2016210396","https://openalex.org/W2089217930","https://openalex.org/W2105624819","https://openalex.org/W2113207845","https://openalex.org/W2296059279","https://openalex.org/W2312594268","https://openalex.org/W2340791329","https://openalex.org/W2344428291","https://openalex.org/W2481907963","https://openalex.org/W2511021861","https://openalex.org/W2517580759","https://openalex.org/W2585908831","https://openalex.org/W2745254868","https://openalex.org/W2750304600","https://openalex.org/W2766228243","https://openalex.org/W2772858445","https://openalex.org/W2799827709","https://openalex.org/W2810041204","https://openalex.org/W2897977943","https://openalex.org/W2900645283","https://openalex.org/W2912610170","https://openalex.org/W2964121744","https://openalex.org/W2975879372","https://openalex.org/W2982562347","https://openalex.org/W3021969420","https://openalex.org/W3159580687","https://openalex.org/W3197942674","https://openalex.org/W4241653265","https://openalex.org/W6631190155","https://openalex.org/W6677088747","https://openalex.org/W6697422243","https://openalex.org/W6742804677","https://openalex.org/W6768118886","https://openalex.org/W6781775558"],"related_works":["https://openalex.org/W4206511378","https://openalex.org/W4206618949","https://openalex.org/W2526321210","https://openalex.org/W3205863630","https://openalex.org/W4318833145","https://openalex.org/W2364275385","https://openalex.org/W4388704167","https://openalex.org/W2007977664","https://openalex.org/W4376874882","https://openalex.org/W2224749288"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3],"propose":[4],"a":[5,20],"Quantile":[6],"Regression":[7],"Deep":[8],"Neural":[9],"Network":[10],"capable":[11],"of":[12,33,78],"forecasting":[13,65],"multiple":[14],"quantiles":[15],"in":[16,55,60,103],"one":[17],"model":[18,101],"using":[19,86],"combined":[21],"quantile":[22,49],"loss":[23,44],"function,":[24],"and":[25,94],"apply":[26],"it":[27],"to":[28,52,74,99],"probabilistically":[29],"forecast":[30,71],"the":[31,42,48,75,79],"prices":[32],"8":[34],"European":[35],"Day":[36],"Ahead":[37],"Markets.":[38],"We":[39],"show":[40],"that":[41],"proposed":[43],"function":[45],"significantly":[46],"reduces":[47],"crossing":[50],"problem":[51],"(near)":[53],"0%":[54],"all":[56,104],"markets":[57],"considered,":[58],"while":[59],"some":[61],"cases":[62],"simultaneously":[63],"increasing":[64],"performance":[66,102],"based":[67],"on":[68],"classical":[69],"point":[70],"metrics":[72],"applied":[73],"expected":[76],"value":[77],"probabilistic":[80],"forecast.":[81],"The":[82],"models":[83],"are":[84],"optimized":[85],"an":[87,91],"automated":[88],"approach":[89],"with":[90],"elaborate":[92],"feature-":[93],"hyperparameter":[95],"search":[96],"space,":[97],"leading":[98],"good":[100],"considered":[105],"markets.":[106]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}