{"id":"https://openalex.org/W2990232357","doi":"https://doi.org/10.1109/isgteurope.2019.8905533","title":"Benchmarking regression methods for function approximation in reinforcement learning: heat pump control","display_name":"Benchmarking regression methods for function approximation in reinforcement learning: heat pump control","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2990232357","doi":"https://doi.org/10.1109/isgteurope.2019.8905533","mag":"2990232357"},"language":"en","primary_location":{"id":"doi:10.1109/isgteurope.2019.8905533","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgteurope.2019.8905533","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)","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/A5057621400","display_name":"Brida V. Mbuwir","orcid":"https://orcid.org/0000-0001-5523-7783"},"institutions":[{"id":"https://openalex.org/I68522396","display_name":"Flemish Institute for Technological Research","ror":"https://ror.org/04gq0w522","country_code":"BE","type":"facility","lineage":["https://openalex.org/I68522396"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Brida V. Mbuwir","raw_affiliation_strings":["AMO, Flemish Institute for Technological Research (VITO),Boeretang 200, Mol,Belgium,2400","AMO, Flemish Institute for Technological Research (VITO), Boeretang 200, Mol, Belgium"],"affiliations":[{"raw_affiliation_string":"AMO, Flemish Institute for Technological Research (VITO),Boeretang 200, Mol,Belgium,2400","institution_ids":["https://openalex.org/I68522396"]},{"raw_affiliation_string":"AMO, Flemish Institute for Technological Research (VITO), Boeretang 200, Mol, Belgium","institution_ids":["https://openalex.org/I68522396"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008706525","display_name":"Fred Spiessens","orcid":"https://orcid.org/0000-0002-1493-6745"},"institutions":[{"id":"https://openalex.org/I68522396","display_name":"Flemish Institute for Technological Research","ror":"https://ror.org/04gq0w522","country_code":"BE","type":"facility","lineage":["https://openalex.org/I68522396"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Fred Spiessens","raw_affiliation_strings":["AMO, Flemish Institute for Technological Research (VITO),Boeretang 200, Mol,Belgium,2400","AMO, Flemish Institute for Technological Research (VITO), Boeretang 200, Mol, Belgium"],"affiliations":[{"raw_affiliation_string":"AMO, Flemish Institute for Technological Research (VITO),Boeretang 200, Mol,Belgium,2400","institution_ids":["https://openalex.org/I68522396"]},{"raw_affiliation_string":"AMO, Flemish Institute for Technological Research (VITO), Boeretang 200, Mol, Belgium","institution_ids":["https://openalex.org/I68522396"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024462942","display_name":"Geert Deconinck","orcid":"https://orcid.org/0000-0002-2225-3987"},"institutions":[{"id":"https://openalex.org/I89206478","display_name":"Engie (Belgium)","ror":"https://ror.org/00c97z041","country_code":"BE","type":"company","lineage":["https://openalex.org/I4210124897","https://openalex.org/I89206478"]},{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Geert Deconinck","raw_affiliation_strings":["ESAT/ELECTA, KU Leuven,Kasteelpark Arenberg 10, Leuven,Belgium,3001","ESAT/ELECTA, KU Leuven, Kasteelpark Arenberg 10, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"ESAT/ELECTA, KU Leuven,Kasteelpark Arenberg 10, Leuven,Belgium,3001","institution_ids":["https://openalex.org/I99464096"]},{"raw_affiliation_string":"ESAT/ELECTA, KU Leuven, Kasteelpark Arenberg 10, Leuven, Belgium","institution_ids":["https://openalex.org/I89206478","https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057621400"],"corresponding_institution_ids":["https://openalex.org/I68522396"],"apc_list":null,"apc_paid":null,"fwci":0.8401,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.80910459,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9994000196456909,"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/T12676","display_name":"Machine Learning and ELM","score":0.9994000196456909,"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/T10603","display_name":"Smart Grid Energy Management","score":0.9965000152587891,"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/T12794","display_name":"Adaptive Dynamic Programming Control","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/computer-science","display_name":"Computer science","score":0.6614837646484375},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.5785379409790039},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5529492497444153},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5163780450820923},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4689556956291199},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4508884847164154},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4459729790687561},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.43612009286880493},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.41586920619010925},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.25073790550231934},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.2330363392829895},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15634462237358093}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6614837646484375},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.5785379409790039},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5529492497444153},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5163780450820923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4689556956291199},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4508884847164154},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4459729790687561},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.43612009286880493},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.41586920619010925},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25073790550231934},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2330363392829895},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15634462237358093}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isgteurope.2019.8905533","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgteurope.2019.8905533","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8500000238418579}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W166862392","https://openalex.org/W1510052597","https://openalex.org/W1678356000","https://openalex.org/W2056132907","https://openalex.org/W2077440620","https://openalex.org/W2106411961","https://openalex.org/W2111072639","https://openalex.org/W2120346334","https://openalex.org/W2121863487","https://openalex.org/W2270330859","https://openalex.org/W2295598076","https://openalex.org/W2557283755","https://openalex.org/W2605267926","https://openalex.org/W2613191681","https://openalex.org/W2767535599","https://openalex.org/W2768348081","https://openalex.org/W2811015419","https://openalex.org/W3102476541","https://openalex.org/W4214717370","https://openalex.org/W6676179485","https://openalex.org/W6677737365","https://openalex.org/W6745609711"],"related_works":["https://openalex.org/W3118582656","https://openalex.org/W2111712374","https://openalex.org/W3179987986","https://openalex.org/W2625709058","https://openalex.org/W2938445746","https://openalex.org/W2883999368","https://openalex.org/W2165343760","https://openalex.org/W2093824915","https://openalex.org/W2347311020","https://openalex.org/W2592040584","https://openalex.org/W2387941851","https://openalex.org/W2492494189","https://openalex.org/W1665018864","https://openalex.org/W2349898864","https://openalex.org/W2151203917","https://openalex.org/W2106179101","https://openalex.org/W3040733061","https://openalex.org/W2784249404","https://openalex.org/W2067847508","https://openalex.org/W3014747440"],"abstract_inverted_index":{"Motivated":[0],"by":[1,92,219],"the":[2,6,69,72,79,102,142,182,204],"increasing":[3],"interest":[4],"in":[5,55,86,137,172,181,192,211],"application":[7,134],"of":[8,25,71,81,101,116,135,159,178,200,203],"machine":[9],"learning":[10,48,57],"techniques":[11],"for":[12,52,133],"power":[13],"system":[14],"control":[15,80],"and":[16,46,120,146,195],"demand":[17],"response":[18],"applications,":[19],"this":[20],"paper":[21],"presents":[22],"a":[23,76,82,87,93,110,130,157,165,169,176,186,189,197,208,215,220],"benchmark":[24],"regression":[26,44,73],"methods":[27],"(extremely":[28],"randomized":[29],"trees":[30],"(extra-trees),":[31],"multi-layer":[32],"perceptron":[33],"(MLP),":[34],"extreme":[35,47],"gradient":[36,39],"boosting,":[37],"light":[38],"boosting":[40],"machines,":[41],"support":[42],"vector":[43],"(SVR)":[45],"machines":[49],"(ELMs))":[50],"available":[51],"function":[53],"approximation":[54],"reinforcement":[56],"(RL)":[58],"techniques.":[59],"In":[60],"addition,":[61],"we":[62],"use":[63],"Bayesian":[64],"optimization":[65],"to":[66,224],"optimally":[67],"select":[68],"hyperparameters":[70],"algorithms.":[74],"As":[75],"case":[77],"study,":[78],"heat":[83],"pump":[84],"(HP)":[85],"grid-connected":[88],"single-user":[89],"microgrid":[90],"powered":[91],"photovoltaic":[94],"(PV)":[95],"installation":[96],"is":[97,104],"considered.":[98],"The":[99],"operation":[100],"HP":[103],"controlled":[105],"using":[106],"fitted":[107],"Q-iteration":[108],"(FQI),":[109],"batch":[111],"RL":[112],"algorithm,":[113],"with":[114,168,188,196,214],"objective":[115],"maximizing":[117],"PV":[118,173,193,212],"self-consumption":[119,174,194,213],"minimizing":[121],"electricity":[122],"cost.":[123],"Simulation":[124,152],"results":[125,153],"show":[126,155],"that":[127,156,202],"extra-trees":[128,225],"are":[129,149],"suitable":[131],"choice":[132],"FQI":[136],"real":[138],"world":[139],"applications":[140],"where":[141],"importance":[143],"on":[144],"accuracy":[145],"computation":[147,198,216],"times":[148],"equally":[150],"weighted.":[151],"also":[154],"bag":[158],"ELMs":[160],"performs":[161],"better":[162],"than":[163],"(i)":[164],"single":[166],"ELM":[167],"25.8%":[170],"increment":[171],"costing":[175],"factor":[177,221],"3.8":[179],"increase":[180,191],"computational":[183],"time":[184,199,217],"(ii)":[185],"MLP":[187],"5.4%":[190],"25%":[201],"MLP.":[205],"With":[206],"SVR,":[207],"32%":[209],"decrease":[210],"increased":[218],"50":[222],"compared":[223],"was":[226],"obtained.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
