{"id":"https://openalex.org/W2966111888","doi":"https://doi.org/10.3390/sym11080956","title":"Energy Consumption Load Forecasting Using a Level-Based Random Forest Classifier","display_name":"Energy Consumption Load Forecasting Using a Level-Based Random Forest Classifier","publication_year":2019,"publication_date":"2019-07-29","ids":{"openalex":"https://openalex.org/W2966111888","doi":"https://doi.org/10.3390/sym11080956","mag":"2966111888"},"language":"en","primary_location":{"id":"doi:10.3390/sym11080956","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym11080956","pdf_url":"https://www.mdpi.com/2073-8994/11/8/956/pdf?version=1564383578","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"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":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/11/8/956/pdf?version=1564383578","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034471403","display_name":"Yu\u2010Tung Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Tung Chen","raw_affiliation_strings":["Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan","institution_ids":["https://openalex.org/I154864474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040031948","display_name":"Eduardo Piedad","orcid":"https://orcid.org/0000-0002-9723-1731"},"institutions":[{"id":"https://openalex.org/I102822971","display_name":"University of San Jose\u2013Recoletos","ror":"https://ror.org/04jmbkr13","country_code":"PH","type":"education","lineage":["https://openalex.org/I102822971"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Eduardo Piedad","raw_affiliation_strings":["Department of Electrical Engineering, University of San Jose-Recoletos, Cebu City 6000, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of San Jose-Recoletos, Cebu City 6000, Philippines","institution_ids":["https://openalex.org/I102822971"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048873508","display_name":"Cheng\u2010Chien Kuo","orcid":"https://orcid.org/0000-0003-4990-0459"},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Cheng-Chien Kuo","raw_affiliation_strings":["Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan"],"raw_orcid":"https://orcid.org/0000-0003-4990-0459","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan","institution_ids":["https://openalex.org/I154864474"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048873508"],"corresponding_institution_ids":["https://openalex.org/I154864474"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":1.574,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":{"value":0.8375981,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"11","issue":"8","first_page":"956","last_page":"956"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9923999905586243,"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/T12368","display_name":"Grey System Theory Applications","score":0.9861000180244446,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.8517284393310547},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6699167490005493},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6556848883628845},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5871906280517578},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5248472690582275},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5011394023895264},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45628389716148376},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.41535499691963196},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40307527780532837},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3825768232345581},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32898277044296265},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.29753851890563965},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2358686625957489}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8517284393310547},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6699167490005493},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6556848883628845},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5871906280517578},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5248472690582275},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5011394023895264},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45628389716148376},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.41535499691963196},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40307527780532837},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3825768232345581},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32898277044296265},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29753851890563965},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2358686625957489}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym11080956","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym11080956","pdf_url":"https://www.mdpi.com/2073-8994/11/8/956/pdf?version=1564383578","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"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":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:1e7212f8c08f45fe8e1f87442245f223","is_oa":true,"landing_page_url":"https://doaj.org/article/1e7212f8c08f45fe8e1f87442245f223","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 11, Iss 8, p 956 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/11/8/956/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/sym11080956","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym11080956","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym11080956","pdf_url":"https://www.mdpi.com/2073-8994/11/8/956/pdf?version=1564383578","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"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":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2966111888.pdf","grobid_xml":"https://content.openalex.org/works/W2966111888.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W782471358","https://openalex.org/W1720804347","https://openalex.org/W1969885422","https://openalex.org/W1990193139","https://openalex.org/W1990910451","https://openalex.org/W1991277158","https://openalex.org/W1999065216","https://openalex.org/W2029264889","https://openalex.org/W2051607409","https://openalex.org/W2060774500","https://openalex.org/W2064469609","https://openalex.org/W2070757581","https://openalex.org/W2083020303","https://openalex.org/W2091693228","https://openalex.org/W2095190298","https://openalex.org/W2101234009","https://openalex.org/W2133752269","https://openalex.org/W2275088575","https://openalex.org/W2338117734","https://openalex.org/W2585502432","https://openalex.org/W2586259521","https://openalex.org/W2767100009","https://openalex.org/W2885171608","https://openalex.org/W2911964244","https://openalex.org/W2913448648","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W2889302474","https://openalex.org/W2048488252","https://openalex.org/W2940614149","https://openalex.org/W4288365262","https://openalex.org/W2787485953","https://openalex.org/W3217432596","https://openalex.org/W31220157","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397"],"abstract_inverted_index":{"Energy":[0],"consumers":[1],"may":[2],"not":[3],"know":[4],"whether":[5],"their":[6,22],"next-hour":[7],"forecasted":[8],"load":[9],"is":[10,196],"either":[11],"high":[12],"or":[13],"low":[14],"based":[15],"on":[16],"the":[17,41,73,152,164,169,184,187],"actual":[18,42],"value":[19],"predicted":[20],"from":[21],"historical":[23],"data.":[24],"A":[25,61,104],"conventional":[26,165,188],"method":[27,93],"of":[28,171,186],"level":[29,193],"prediction":[30,63,177],"with":[31,64,146],"a":[32,95,100,201],"pattern":[33,54,65,85],"recognition":[34,66,86],"approach":[35],"was":[36,68,88,121],"performed":[37],"by":[38],"first":[39],"predicting":[40],"numerical":[43],"values":[44],"using":[45,76,94],"typical":[46],"pattern-based":[47],"regression":[48,92],"models,":[49],"hen":[50],"classifying":[51],"them":[52],"into":[53],"levels":[55,75,174],"(e.g.,":[56],"low,":[57],"average,":[58],"and":[59,133,141,159,182,199],"high).":[60],"proposed":[62,84,153],"scheme":[67],"developed":[69,191],"to":[70,90,99,180],"directly":[71],"predict":[72],"desired":[74,173],"simpler":[77],"classifier":[78,87,154],"models":[79],"without":[80],"undergoing":[81],"regression.":[82],"The":[83,190],"compared":[89],"its":[91,172,176],"similar":[96,129],"algorithm":[97,108],"applied":[98],"real-world":[101],"energy":[102,192],"dataset.":[103],"random":[105,147],"forest":[106],"(RF)":[107],"which":[109,195],"outperformed":[110],"other":[111],"widely":[112],"used":[113,122,128],"machine":[114],"learning":[115],"(ML)":[116],"techniques":[117],"in":[118,123],"previous":[119],"research":[120],"both":[124],"methods.":[125],"Both":[126],"schemes":[127],"parameters":[130],"for":[131],"training":[132,139],"testing":[134],"simulations.":[135],"After":[136],"10-time":[137],"cross":[138],"validation":[140],"five":[142],"averaged":[143],"repeated":[144],"runs":[145],"permutation":[148],"per":[149],"data":[150],"splitting,":[151],"shows":[155],"better":[156],"computation":[157],"speed":[158],"higher":[160],"classification":[161,203],"accuracy":[162,178,185],"than":[163],"method.":[166,189],"However,":[167],"when":[168],"number":[170],"increases,":[175],"seems":[179],"decrease":[181],"approaches":[183],"prediction,":[194],"computationally":[197],"inexpensive":[198],"has":[200],"good":[202],"performance,":[204],"can":[205],"serve":[206],"as":[207],"an":[208],"alternative":[209],"forecasting":[210],"scheme.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
