{"id":"https://openalex.org/W2473829957","doi":"https://doi.org/10.1109/pscc.2016.7540994","title":"Demand forecasting at low aggregation levels using Factored Conditional Restricted Boltzmann Machine","display_name":"Demand forecasting at low aggregation levels using Factored Conditional Restricted Boltzmann Machine","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2473829957","doi":"https://doi.org/10.1109/pscc.2016.7540994","mag":"2473829957"},"language":"en","primary_location":{"id":"doi:10.1109/pscc.2016.7540994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pscc.2016.7540994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Power Systems Computation Conference (PSCC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.tue.nl/en/publications/6c7bedaa-be5f-4b58-bb73-4a3337f8ca32","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027401676","display_name":"Elena Mocanu","orcid":"https://orcid.org/0000-0002-0856-579X"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Elena Mocanu","raw_affiliation_strings":["Department of Electrical Engineering, Eindhoven University of Technology, Netherlands"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Eindhoven University of Technology, Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074304207","display_name":"Phuong H. Nguyen","orcid":"https://orcid.org/0000-0003-1124-2710"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Phuong H. Nguyen","raw_affiliation_strings":["Department of Electrical Engineering, Eindhoven University of Technology, Netherlands"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Eindhoven University of Technology, Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022437377","display_name":"Madeleine Gibescu","orcid":"https://orcid.org/0000-0002-4420-8538"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Madeleine Gibescu","raw_affiliation_strings":["Department of Electrical Engineering, Eindhoven University of Technology, Netherlands"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Eindhoven University of Technology, Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055976816","display_name":"Emil Mahler Larsen","orcid":null},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Emil Mahler Larsen","raw_affiliation_strings":["Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058698672","display_name":"Pierre Pinson","orcid":"https://orcid.org/0000-0002-1480-0282"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Pierre Pinson","raw_affiliation_strings":["Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5027401676"],"corresponding_institution_ids":["https://openalex.org/I83019370"],"apc_list":null,"apc_paid":null,"fwci":2.8001,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.9108254,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9923999905586243,"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":0.9923999905586243,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9836000204086304,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.982200026512146,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/boltzmann-machine","display_name":"Boltzmann machine","score":0.6758293509483337},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5823342800140381},{"id":"https://openalex.org/keywords/restricted-boltzmann-machine","display_name":"Restricted Boltzmann machine","score":0.4111180305480957},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3243141770362854},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.09462955594062805}],"concepts":[{"id":"https://openalex.org/C192576344","wikidata":"https://www.wikidata.org/wiki/Q194706","display_name":"Boltzmann machine","level":3,"score":0.6758293509483337},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5823342800140381},{"id":"https://openalex.org/C199354608","wikidata":"https://www.wikidata.org/wiki/Q7316287","display_name":"Restricted Boltzmann machine","level":3,"score":0.4111180305480957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3243141770362854},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.09462955594062805}],"mesh":[],"locations_count":7,"locations":[{"id":"doi:10.1109/pscc.2016.7540994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pscc.2016.7540994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Power Systems Computation Conference (PSCC)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.tue.nl:openaire/6c7bedaa-be5f-4b58-bb73-4a3337f8ca32","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/6c7bedaa-be5f-4b58-bb73-4a3337f8ca32","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Mocanu, E, Larsen, E M, Nguyen, P H, Pinson, P & Gibescu, M 2016, Demand forecasting at low aggregation levels using factored conditional restricted Boltzmann machine. in Proceedings of the 19th Power Systems Computation Conference (PSCC), 20-24 June 2016, Genoa, Italy., 7540994, Institute of Electrical and Electronics Engineers, Piscataway, pp. 1-7, 19th Power Systems Computation Conference (PSCC 2016), June 20-24, 2016, Genoa, Italy, Genoa, Italy, 20/06/16. https://doi.org/10.1109/PSCC.2016.7540994","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:888838","is_oa":false,"landing_page_url":"http://library.tue.nl/csp/dare/LinkToRepository.csp?recordnumber=888838","pdf_url":null,"source":{"id":"https://openalex.org/S4306400553","display_name":"Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I8204097","host_organization_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","host_organization_lineage":["https://openalex.org/I8204097"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:library.tue.nl:841339","is_oa":false,"landing_page_url":"http://repository.tue.nl/841339","pdf_url":null,"source":{"id":"https://openalex.org/S4406923046","display_name":"TU/e Research Portal (Eindhoven University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:library.tue.nl:888838","is_oa":false,"landing_page_url":"http://repository.tue.nl/888838","pdf_url":null,"source":{"id":"https://openalex.org/S4406923046","display_name":"TU/e Research Portal (Eindhoven University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:pure.atira.dk:publications/12fc696c-1e6a-449e-84db-9474b553a259","is_oa":true,"landing_page_url":"https://orbit.dtu.dk/en/publications/12fc696c-1e6a-449e-84db-9474b553a259","pdf_url":null,"source":{"id":"https://openalex.org/S4306400705","display_name":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I96673099","host_organization_name":"Technical University of Denmark","host_organization_lineage":["https://openalex.org/I96673099"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Mocanu , E , Nguyen , P H , Gibescu , M , Larsen , E M &amp; Pinson , P 2016 , Demand Forecasting at Low Aggregation Levels\u00a0using Factored Conditional Restricted Boltzmann\u00a0Machine. in Proceedings of 19th Power Systems Computation Conference. . IEEE , 19th Power Systems Computation Conference , Genoa , Italy , 20/06/2016 . https://doi.org/10.1109/PSCC.2016.7540994","raw_type":"contributionToPeriodical"},{"id":"pmh:tue:oai:pure.tue.nl:publications/6c7bedaa-be5f-4b58-bb73-4a3337f8ca32","is_oa":true,"landing_page_url":"https://research.tue.nl/nl/publications/6c7bedaa-be5f-4b58-bb73-4a3337f8ca32","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the 19th Power Systems Computation Conference (PSCC), 20-24 June 2016, Genoa, Italy, 1 - 7","raw_type":"info:eu-repo/semantics/conferencepaper"}],"best_oa_location":{"id":"pmh:oai:pure.tue.nl:openaire/6c7bedaa-be5f-4b58-bb73-4a3337f8ca32","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/6c7bedaa-be5f-4b58-bb73-4a3337f8ca32","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Mocanu, E, Larsen, E M, Nguyen, P H, Pinson, P & Gibescu, M 2016, Demand forecasting at low aggregation levels using factored conditional restricted Boltzmann machine. in Proceedings of the 19th Power Systems Computation Conference (PSCC), 20-24 June 2016, Genoa, Italy., 7540994, Institute of Electrical and Electronics Engineers, Piscataway, pp. 1-7, 19th Power Systems Computation Conference (PSCC 2016), June 20-24, 2016, Genoa, Italy, Genoa, Italy, 20/06/16. https://doi.org/10.1109/PSCC.2016.7540994","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"score":0.8799999952316284,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W44815768","https://openalex.org/W97016928","https://openalex.org/W1495290276","https://openalex.org/W1519010609","https://openalex.org/W1697053819","https://openalex.org/W1813659000","https://openalex.org/W1862389813","https://openalex.org/W1964155876","https://openalex.org/W1972283660","https://openalex.org/W1980618555","https://openalex.org/W1984703120","https://openalex.org/W1995175359","https://openalex.org/W1997334587","https://openalex.org/W2023460138","https://openalex.org/W2033065921","https://openalex.org/W2042131079","https://openalex.org/W2051607409","https://openalex.org/W2071258353","https://openalex.org/W2072128103","https://openalex.org/W2099866409","https://openalex.org/W2100495367","https://openalex.org/W2101651041","https://openalex.org/W2106262311","https://openalex.org/W2109316012","https://openalex.org/W2116064496","https://openalex.org/W2153635508","https://openalex.org/W2161257182","https://openalex.org/W2883511548","https://openalex.org/W4231109964","https://openalex.org/W6603960082","https://openalex.org/W6631071709","https://openalex.org/W6637189935","https://openalex.org/W6638304892","https://openalex.org/W6638892135"],"related_works":["https://openalex.org/W2952018105","https://openalex.org/W2916681395","https://openalex.org/W4283272532","https://openalex.org/W2119341610","https://openalex.org/W2556473569","https://openalex.org/W2193475944","https://openalex.org/W4302433642","https://openalex.org/W2529583158","https://openalex.org/W2551541394","https://openalex.org/W2892911634"],"abstract_inverted_index":{"The":[0,105,128,145],"electrical":[1],"demand":[2,103],"forecasting":[3],"problem":[4,14,153],"can":[5],"be":[6],"regarded":[7],"as":[8],"a":[9,82],"non-linear":[10],"time":[11,38,89],"series":[12,39,90],"prediction":[13,56,66,152],"depending":[15],"on":[16,109],"many":[17],"complex":[18],"factors":[19],"since":[20],"it":[21,101],"is":[22,107],"required":[23],"at":[24,29],"various":[25,37],"aggregation":[26],"levels":[27],"and":[28,40,71,99,121,135],"high":[30],"resolution.":[31],"To":[32],"solve":[33],"this":[34,78],"challenging":[35],"problem,":[36],"machine":[41,160],"learning":[42,59,86,161],"approaches":[43],"has":[44],"been":[45],"proposed":[46],"in":[47],"the":[48,65,110,150,158],"literature.":[49],"As":[50],"an":[51],"evolution":[52],"of":[53,115,139],"neural":[54],"network-based":[55],"methods,":[57],"deep":[58,85],"techniques":[60],"are":[61,130],"expected":[62],"to":[63,141],"increase":[64],"accuracy":[67],"by":[68],"being":[69],"stochastic":[70],"allowing":[72],"bi-directional":[73],"connections":[74],"between":[75],"neurons.":[76],"In":[77],"paper,":[79],"we":[80],"investigate":[81],"newly":[83],"developed":[84],"model":[87],"for":[88,102,149],"prediction,":[91],"namely":[92],"Factored":[93],"Conditional":[94],"Restricted":[95],"Boltzmann":[96],"Machine":[97],"(FCRBM),":[98],"extend":[100],"forecasting.":[104],"assessment":[106],"made":[108],"EcoGrid":[111],"EU":[112],"dataset,":[113],"consisting":[114],"aggregated":[116],"electric":[117],"power":[118],"consumption,":[119],"price":[120],"meteorological":[122],"data":[123],"collected":[124],"from":[125],"1900":[126],"customers.":[127],"households":[129],"equipped":[131],"with":[132],"local":[133],"generation":[134],"smart":[136],"appliances":[137],"capable":[138],"responding":[140],"real-time":[142],"pricing":[143],"signals.":[144],"results":[146],"show":[147],"that":[148],"energy":[151],"solved":[154],"here,":[155],"FCRBM":[156],"outperforms":[157],"benchmark":[159],"approach,":[162],"i.e.":[163],"Support":[164],"Vector":[165],"Machine.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
