{"id":"https://openalex.org/W3202786448","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534134","title":"Pattern-based Forecasting of Monthly Electricity Demand using Support Vector Machine","display_name":"Pattern-based Forecasting of Monthly Electricity Demand using Support Vector Machine","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3202786448","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534134","mag":"3202786448"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534134","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534134","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5082473388","display_name":"Pawe\u0142 Pe\u0142ka","orcid":"https://orcid.org/0000-0002-2609-811X"},"institutions":[{"id":"https://openalex.org/I130294970","display_name":"Cz\u0119stochowa University of Technology","ror":"https://ror.org/046awyn59","country_code":"PL","type":"education","lineage":["https://openalex.org/I130294970"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Pawel Pelka","raw_affiliation_strings":["Electrical Engineering Faculty, Czestochowa University of Technology, Czestochowa, Poland"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Faculty, Czestochowa University of Technology, Czestochowa, Poland","institution_ids":["https://openalex.org/I130294970"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5082473388"],"corresponding_institution_ids":["https://openalex.org/I130294970"],"apc_list":null,"apc_paid":null,"fwci":1.5041,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.82436816,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":1.0,"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":1.0,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9973000288009644,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9908999800682068,"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/exponential-smoothing","display_name":"Exponential smoothing","score":0.879567563533783},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.781977653503418},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6835589408874512},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5972844958305359},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.5812241435050964},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5617544054985046},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.46480074524879456},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.44958969950675964},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4359697699546814},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.43461644649505615},{"id":"https://openalex.org/keywords/moving-average","display_name":"Moving average","score":0.4327136278152466},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3607478737831116},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34138455986976624},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2979133725166321},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.24580791592597961},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2395561933517456},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.23405444622039795},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14296984672546387}],"concepts":[{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.879567563533783},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.781977653503418},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6835589408874512},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5972844958305359},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.5812241435050964},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5617544054985046},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.46480074524879456},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.44958969950675964},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4359697699546814},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.43461644649505615},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.4327136278152466},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3607478737831116},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34138455986976624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2979133725166321},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.24580791592597961},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2395561933517456},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.23405444622039795},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14296984672546387},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534134","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534134","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W993182060","https://openalex.org/W1512101952","https://openalex.org/W2056489048","https://openalex.org/W2056583509","https://openalex.org/W2069889024","https://openalex.org/W2081060955","https://openalex.org/W2085155527","https://openalex.org/W2105916576","https://openalex.org/W2123535552","https://openalex.org/W2134325661","https://openalex.org/W2156909104","https://openalex.org/W2357026187","https://openalex.org/W2604710444","https://openalex.org/W2750652713","https://openalex.org/W2899494475","https://openalex.org/W2945839724","https://openalex.org/W3090860962","https://openalex.org/W4230410911","https://openalex.org/W4230674625"],"related_works":["https://openalex.org/W4387220233","https://openalex.org/W2955496313","https://openalex.org/W4220924527","https://openalex.org/W3097024643","https://openalex.org/W3215481666","https://openalex.org/W4210402713","https://openalex.org/W4220732081","https://openalex.org/W2024558842","https://openalex.org/W29970211","https://openalex.org/W3181168211"],"abstract_inverted_index":{"Forecasting":[0],"mid-term":[1],"electricity":[2,26],"demand":[3,27,43,50,68,130],"has":[4],"become":[5],"an":[6],"important":[7],"tool":[8],"for":[9,23],"energy":[10,49,129],"management,":[11],"maintenance":[12],"planning,":[13],"and":[14,41,58,81,113,148,160],"power":[15],"system":[16],"operation.":[17],"This":[18],"article":[19],"provides":[20],"a":[21,30,117],"solution":[22],"predicting":[24],"monthly":[25,67],"based":[28,103],"on":[29,104],"Support":[31,120],"Vector":[32,121],"Machine":[33,122],"(SVM),":[34],"which":[35],"approximates":[36],"the":[37,63,66,79,91,96,119,127,157],"relationship":[38],"between":[39],"historical":[40,105],"future":[42],"patterns.":[44],"The":[45,85,98],"time":[46,69],"series":[47,70],"of":[48,162],"shows":[51],"instability,":[52],"seasonal":[53],"fluctuation":[54],"cycles,":[55],"long-term":[56],"trends,":[57],"random":[59],"noise.":[60],"To":[61],"simplify":[62],"forecasting":[64],"problem,":[65],"is":[71,88],"represented":[72],"by":[73],"annual":[74],"cycle":[75],"patterns":[76],"that":[77,94],"unify":[78],"data":[80,108],"filter":[82],"out":[83],"trends.":[84],"output":[86],"variable":[87,93],"encoded":[89],"using":[90,109],"encoding":[92,99],"describes":[95],"process.":[97],"variables":[100],"are":[101],"determined":[102],"or":[106],"forecasted":[107],"statistical":[110],"methods":[111],"ARIMA":[112],"exponential":[114,142],"smoothing.":[115],"As":[116],"test,":[118],"model":[123],"was":[124],"applied":[125],"to":[126],"month-to-month":[128],"forecasts":[131],"in":[132],"European":[133],"countries.":[134],"Compared":[135],"with":[136],"other":[137],"models":[138],"such":[139],"as":[140],"ARIMA,":[141],"smoothing,":[143],"multilayer":[144],"perceptron,":[145],"neuro-fuzzy":[146],"systems":[147],"long-short":[149],"term":[150],"memory":[151],"type":[152],"network,":[153],"this":[154,163],"result":[155],"confirms":[156],"high":[158],"performance":[159],"competitiveness":[161],"model.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2}],"updated_date":"2026-02-25T21:11:00.739837","created_date":"2025-10-10T00:00:00"}
