{"id":"https://openalex.org/W4212919661","doi":"https://doi.org/10.1109/tencon54134.2021.9707319","title":"A Deep Learning Technique for Electricity Price Forecasting in Consideration of Spikes","display_name":"A Deep Learning Technique for Electricity Price Forecasting in Consideration of Spikes","publication_year":2021,"publication_date":"2021-12-07","ids":{"openalex":"https://openalex.org/W4212919661","doi":"https://doi.org/10.1109/tencon54134.2021.9707319"},"language":"en","primary_location":{"id":"doi:10.1109/tencon54134.2021.9707319","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon54134.2021.9707319","pdf_url":null,"source":{"id":"https://openalex.org/S4363608113","display_name":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","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/A5031673452","display_name":"Kodai Yamada","orcid":null},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kodai Yamada","raw_affiliation_strings":["Meiji University,Department of Network Design,Tokyo,Japan,164-8525"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meiji University,Department of Network Design,Tokyo,Japan,164-8525","institution_ids":["https://openalex.org/I16656306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016452461","display_name":"Hiroyuki Mori","orcid":"https://orcid.org/0000-0002-1253-1840"},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroyuki Mori","raw_affiliation_strings":["Meiji University,Department of Network Design,Tokyo,Japan,164-8525"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meiji University,Department of Network Design,Tokyo,Japan,164-8525","institution_ids":["https://openalex.org/I16656306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3005,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79626712,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"744","last_page":"749"},"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/T10424","display_name":"Electric Power System Optimization","score":0.995199978351593,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.993399977684021,"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/autoencoder","display_name":"Autoencoder","score":0.8475801944732666},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7245841026306152},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6962143182754517},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6947200894355774},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5922472476959229},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5702359676361084},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.535425066947937},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4825698435306549},{"id":"https://openalex.org/keywords/electricity-price-forecasting","display_name":"Electricity price forecasting","score":0.46761855483055115},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.46679338812828064},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.45583653450012207},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.41794341802597046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4053756594657898},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.390980988740921},{"id":"https://openalex.org/keywords/electricity-price","display_name":"Electricity price","score":0.28564587235450745},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15816405415534973}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8475801944732666},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7245841026306152},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6962143182754517},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6947200894355774},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5922472476959229},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5702359676361084},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.535425066947937},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4825698435306549},{"id":"https://openalex.org/C2781104810","wikidata":"https://www.wikidata.org/wiki/Q23580049","display_name":"Electricity price forecasting","level":4,"score":0.46761855483055115},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.46679338812828064},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.45583653450012207},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.41794341802597046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4053756594657898},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.390980988740921},{"id":"https://openalex.org/C2983129042","wikidata":"https://www.wikidata.org/wiki/Q870344","display_name":"Electricity price","level":3,"score":0.28564587235450745},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15816405415534973},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon54134.2021.9707319","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon54134.2021.9707319","pdf_url":null,"source":{"id":"https://openalex.org/S4363608113","display_name":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"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":26,"referenced_works":["https://openalex.org/W1594031697","https://openalex.org/W1899504021","https://openalex.org/W2025768430","https://openalex.org/W2070665556","https://openalex.org/W2078462325","https://openalex.org/W2079954576","https://openalex.org/W2094208771","https://openalex.org/W2100495367","https://openalex.org/W2106536947","https://openalex.org/W2111406701","https://openalex.org/W2126831543","https://openalex.org/W2127218421","https://openalex.org/W2148780454","https://openalex.org/W2151291650","https://openalex.org/W2155482907","https://openalex.org/W2170831719","https://openalex.org/W2320477162","https://openalex.org/W2335446118","https://openalex.org/W2511296364","https://openalex.org/W2563525717","https://openalex.org/W2914588191","https://openalex.org/W3016489462","https://openalex.org/W4236876307","https://openalex.org/W6631636882","https://openalex.org/W6676083213","https://openalex.org/W6678914141"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2997921738","https://openalex.org/W2965146396","https://openalex.org/W2770818364","https://openalex.org/W3033563910"],"abstract_inverted_index":{"This":[0,42,56],"paper":[1,43,57,76],"presents":[2,77],"a":[3,78,103],"Deep":[4,67],"Neural":[5],"Network":[6],"(DNN)":[7],"method":[8,81,131],"for":[9,134],"electricity":[10],"price":[11],"forecasting":[12,54],"in":[13,66,102],"power":[14],"markets.":[15],"They":[16],"are":[17,23],"inclined":[18],"to":[19,25,40,51,123],"generate":[20],"spikes":[21,50],"that":[22,35,82,105],"dozens":[24],"hundred":[26],"times":[27],"as":[28,30],"large":[29],"the":[31,36,47,53,60,71,93,119,129],"normal":[32],"prices":[33],"so":[34],"prediction":[37,48],"is":[38,97,109,132],"hard":[39],"handle.":[41],"focuses":[44],"attention":[45],"on":[46],"of":[49,63,73,84,90,100,116,128,136],"suppress":[52],"errors.":[55],"deals":[58],"with":[59,92,111],"pretraining":[61],"technique":[62],"Autoencoder":[64],"(AE)":[65],"Learning.":[68],"To":[69],"enhance":[70],"performance":[72],"AE,":[74],"this":[75],"Denoising-Autoencoder":[79],"(DAE)-based":[80],"consists":[83],"DAE":[85,96],"and":[86],"Multilayer":[87],"Perceptron":[88],"(MLP)":[89],"ANN":[91],"clustering":[94,117],"technique.":[95],"an":[98],"extension":[99],"AE":[101],"sense":[104],"noisy":[106],"learning":[107],"data":[108,124,135],"used":[110],"random":[112],"numbers.":[113],"The":[114,126],"use":[115],"enhances":[118],"model":[120],"accuracy":[121],"due":[122],"similarity.":[125],"effectiveness":[127],"proposed":[130],"tested":[133],"New":[137],"England":[138],"ISO,":[139],"USA.":[140]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}