{"id":"https://openalex.org/W2808145089","doi":"https://doi.org/10.1109/isgt-asia.2017.8378323","title":"A morphological filter-based local prediction method with multi-variable inputs for short-term load forecast","display_name":"A morphological filter-based local prediction method with multi-variable inputs for short-term load forecast","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2808145089","doi":"https://doi.org/10.1109/isgt-asia.2017.8378323","mag":"2808145089"},"language":"en","primary_location":{"id":"doi:10.1109/isgt-asia.2017.8378323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgt-asia.2017.8378323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)","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/A5024739714","display_name":"X. Z. Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"X. Z. Ye","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology (SCUT), Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology (SCUT), Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020048163","display_name":"Tianyao Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"T. Y. Ji","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology (SCUT), Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology (SCUT), Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009075081","display_name":"Mengshi Li","orcid":"https://orcid.org/0000-0001-7044-1782"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"M. S. Li","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology (SCUT), Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology (SCUT), Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016939864","display_name":"Qinghua Wu","orcid":"https://orcid.org/0000-0002-0598-8367"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Q. H. Wu","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology (SCUT), Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology (SCUT), Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5024739714"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.5734,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.71309055,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"32","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":0.9998000264167786,"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":0.9998000264167786,"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/T12368","display_name":"Grey System Theory Applications","score":0.9896000027656555,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9866999983787537,"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/support-vector-machine","display_name":"Support vector machine","score":0.5932450294494629},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5310941934585571},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.5244541168212891},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5150127410888672},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5097519755363464},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.49339213967323303},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.45989707112312317},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4510972201824188},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3580910563468933},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3532804846763611},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3432692885398865},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3280371427536011},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3128081262111664},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22820809483528137},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1730552613735199}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5932450294494629},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5310941934585571},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5244541168212891},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5150127410888672},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5097519755363464},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.49339213967323303},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.45989707112312317},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4510972201824188},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3580910563468933},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3532804846763611},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3432692885398865},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3280371427536011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3128081262111664},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22820809483528137},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1730552613735199},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isgt-asia.2017.8378323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgt-asia.2017.8378323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1480674897","https://openalex.org/W1572985415","https://openalex.org/W1598093137","https://openalex.org/W2019192363","https://openalex.org/W2039346833","https://openalex.org/W2080822545","https://openalex.org/W2123792643","https://openalex.org/W2165427810","https://openalex.org/W2239143195","https://openalex.org/W2321536237","https://openalex.org/W2335865153","https://openalex.org/W2337474653","https://openalex.org/W2342107842","https://openalex.org/W2465455179","https://openalex.org/W2491804929","https://openalex.org/W2508682153","https://openalex.org/W2528991849","https://openalex.org/W2562671631","https://openalex.org/W6634491230","https://openalex.org/W6635631360","https://openalex.org/W6677957879","https://openalex.org/W6703576178","https://openalex.org/W6704459710","https://openalex.org/W6719327808","https://openalex.org/W6724868108"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W2012353789","https://openalex.org/W42295635","https://openalex.org/W2530420969","https://openalex.org/W2051187167"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,18,38,43,68,101,107],"prediction":[4],"model":[5,185],"for":[6],"short-":[7],"term":[8],"electric":[9],"load":[10,30,46],"forecast":[11,108,119],"based":[12],"on":[13,153],"Local":[14],"Prediction":[15],"(LP)":[16],"with":[17,32,162],"dual-SE":[19],"weighted":[20],"morphological":[21,39],"filter":[22,40],"derived":[23],"from":[24],"Mathematical":[25],"Morphology":[26],"(MFLP).":[27],"The":[28,110,174],"historical":[29],"data":[31,154],"frequent":[33],"fluctuations":[34],"is":[35,65],"processed":[36],"by":[37,72,92,131,156],"to":[41,105,117,123,138],"obtain":[42],"relatively":[44],"smooth":[45],"curve":[47],"and":[48,82,100,128,158,170,180],"meanwhile":[49],"reserve":[50],"the":[51,54,59,61,73,89,118,125,140,143,163,178,183,190],"characteristics":[52],"of":[53,142,182],"load.":[55],"After":[56],"filtering":[57],"out":[58],"volatility,":[60],"obtained":[62],"time":[63],"series":[64,99],"embedded":[66],"into":[67],"high-dimension":[69],"phase":[70],"space":[71],"LP.":[74],"Moreover,":[75],"weather":[76,102],"conditions":[77],"such":[78],"as":[79,95],"local":[80],"temperature":[81,98],"humidity":[83],"can":[84],"also":[85],"be":[86],"involved":[87],"in":[88,160],"proposed":[90,144,184],"MFLP,":[91],"embedding":[93],"them":[94],"an":[96],"individual":[97],"series,":[103],"respectively,":[104,152],"form":[106],"sample.":[109],"nearest":[111],"neighbours":[112],"who":[113],"have":[114,148],"high":[115],"similarity":[116],"sample":[120],"are":[121,186],"selected":[122],"construct":[124],"training":[126],"set":[127],"then":[129],"predicted":[130],"Support":[132],"Vector":[133],"Regression":[134],"(SVR).":[135],"In":[136],"order":[137],"evaluate":[139],"performance":[141],"model,":[145],"simulation":[146],"studies":[147],"been":[149],"carried":[150],"out,":[151],"collected":[155],"AEMO":[157],"Elia,":[159],"comparison":[161],"SVR,":[164],"Back":[165],"Propagation":[166],"Neural":[167],"Network":[168],"(BPNN)":[169],"persistence":[171],"(Per.)":[172],"models.":[173,192],"results":[175],"demonstrate":[176],"that":[177],"accuracy":[179],"stability":[181],"much":[187],"better":[188],"than":[189],"traditional":[191]},"counts_by_year":[{"year":2019,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
