{"id":"https://openalex.org/W2764307998","doi":"https://doi.org/10.1145/3132479.3132481","title":"Short term load forecasting based on wavelet decomposition and random forest","display_name":"Short term load forecasting based on wavelet decomposition and random forest","publication_year":2017,"publication_date":"2017-10-03","ids":{"openalex":"https://openalex.org/W2764307998","doi":"https://doi.org/10.1145/3132479.3132481","mag":"2764307998"},"language":"en","primary_location":{"id":"doi:10.1145/3132479.3132481","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132479.3132481","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Workshop on Smart Internet of Things","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/A5013797232","display_name":"Qingping Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qingping Huang","raw_affiliation_strings":["North China Electric Power University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018977663","display_name":"Yujiao Li","orcid":null},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujiao Li","raw_affiliation_strings":["North China Electric Power University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100339609","display_name":"Song Liu","orcid":"https://orcid.org/0000-0003-3112-8907"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Song Liu","raw_affiliation_strings":["North China Electric Power University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100346761","display_name":"Peng Liu","orcid":"https://orcid.org/0000-0001-7838-4436"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Liu","raw_affiliation_strings":["North China Electric Power University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5013797232"],"corresponding_institution_ids":["https://openalex.org/I153473198"],"apc_list":null,"apc_paid":null,"fwci":0.7167,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.73484901,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9987999796867371,"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":0.9987999796867371,"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/T14392","display_name":"Geoscience and Mining Technology","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T12368","display_name":"Grey System Theory Applications","score":0.9926999807357788,"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":0.7516055703163147},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7098990082740784},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6621283292770386},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6545323133468628},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.6514903903007507},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6057718992233276},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5119105577468872},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4759249985218048},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4537525177001953},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4477611780166626},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.43480977416038513},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3813496232032776},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3537377119064331},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12974044680595398}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7516055703163147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7098990082740784},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6621283292770386},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6545323133468628},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6514903903007507},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6057718992233276},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5119105577468872},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4759249985218048},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4537525177001953},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4477611780166626},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.43480977416038513},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3813496232032776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3537377119064331},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12974044680595398},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3132479.3132481","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132479.3132481","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Workshop on Smart Internet of Things","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.6499999761581421}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2013570992","https://openalex.org/W2029623062","https://openalex.org/W2039346833","https://openalex.org/W2104571697","https://openalex.org/W2145509823","https://openalex.org/W2171878998","https://openalex.org/W2337474653","https://openalex.org/W2911964244","https://openalex.org/W2948214725","https://openalex.org/W2998312361","https://openalex.org/W4212883601","https://openalex.org/W7043830206","https://openalex.org/W7063872374"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W4396689146","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W2004826645","https://openalex.org/W2077021924"],"abstract_inverted_index":{"Data":[0],"mining":[1,18],"algorithm":[2,78,99],"1":[3],"are":[4,26,118],"main":[5],"stream":[6],"and":[7,22,45,66,108,116,120,163],"intelligent":[8],"algorithms":[9,19],"for":[10,122],"short":[11,123],"term":[12,124],"load":[13,59,86,125,135,139],"forecasting.":[14,136],"However,":[15],"traditional":[16,156],"data":[17,140],"(neural":[20],"network":[21],"support":[23,160],"vector":[24,161],"machine)":[25],"subject":[27],"to":[28,37,47,83,130],"some":[29],"hardly":[30],"conquerable":[31],"drawbacks":[32],"such":[33],"as":[34,90],"being":[35],"easy":[36],"fall":[38],"into":[39],"local":[40],"optimum,":[41],"poor":[42],"generalization":[43],"capability,":[44],"difficult":[46],"determine":[48],"the":[49,53,73,85,91,95,103,111,132,155,167],"model.":[50],"To":[51],"overcome":[52],"above":[54],"issues,":[55],"a":[56,80,144],"new":[57],"short-term":[58],"forecasting":[60,172],"method":[61,82,169],"based":[62],"on":[63],"wavelet":[64,76],"decomposition":[65,77],"random":[67],"forest":[68],"regression(RF)":[69],"is":[70,79,128,141],"proposed.":[71],"On":[72,94],"one":[74],"hand,":[75,97],"valid":[81],"extract":[84],"of":[87,105,134,147],"different":[88],"components":[89],"training":[92],"set.":[93],"other":[96],"RF":[98,117],"suffers":[100],"less":[101],"from":[102,143],"problem":[104],"over":[106],"fitting":[107],"determining":[109],"difficultly":[110],"model":[112],"parameters.":[113],"Wavelet":[114],"analysis":[115],"introduced":[119],"applied":[121],"forecasting,":[126],"which":[127],"helpful":[129],"improve":[131],"accuracy":[133],"The":[137],"historic":[138],"selected":[142],"certain":[145],"area":[146],"Anhui":[148],"province":[149],"in":[150],"this":[151],"paper.":[152],"Compared":[153],"with":[154],"BP":[157],"neural":[158],"network,":[159],"machine,":[162],"not":[164],"improved":[165],"RF,":[166],"proposed":[168],"has":[170],"higher":[171],"accuracy.":[173]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}