{"id":"https://openalex.org/W2951565690","doi":"https://doi.org/10.1145/3307772.3330167","title":"Load Forecasting in Energy Markets","display_name":"Load Forecasting in Energy Markets","publication_year":2019,"publication_date":"2019-06-13","ids":{"openalex":"https://openalex.org/W2951565690","doi":"https://doi.org/10.1145/3307772.3330167","mag":"2951565690"},"language":"en","primary_location":{"id":"doi:10.1145/3307772.3330167","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3307772.3330167","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM International Conference on Future Energy Systems","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/A5060123580","display_name":"Narendhar Gugulothu","orcid":"https://orcid.org/0000-0003-0793-8192"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Narendhar Gugulothu","raw_affiliation_strings":["TCS Research, Hyderabad, India"],"affiliations":[{"raw_affiliation_string":"TCS Research, Hyderabad, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010829519","display_name":"Easwar Subramanian","orcid":"https://orcid.org/0000-0002-9120-6164"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Easwar Subramanian","raw_affiliation_strings":["TCS Research, Hyderabad, India"],"affiliations":[{"raw_affiliation_string":"TCS Research, Hyderabad, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5060123580"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1192,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.44571594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"403","last_page":"405"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9943000078201294,"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"}},{"id":"https://openalex.org/T10424","display_name":"Electric Power System Optimization","score":0.9850999712944031,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7401708364486694},{"id":"https://openalex.org/keywords/feed-forward","display_name":"Feed forward","score":0.710864782333374},{"id":"https://openalex.org/keywords/electricity","display_name":"Electricity","score":0.5886011719703674},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5782439708709717},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5298454761505127},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5169350504875183},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5164187550544739},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5079229474067688},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.49671870470046997},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4567881226539612},{"id":"https://openalex.org/keywords/feedforward-neural-network","display_name":"Feedforward neural network","score":0.4558003544807434},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.443726122379303},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.42723312973976135},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.42272013425827026},{"id":"https://openalex.org/keywords/consumption","display_name":"Consumption (sociology)","score":0.4159095287322998},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.1804766058921814},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1531819999217987},{"id":"https://openalex.org/keywords/control-engineering","display_name":"Control engineering","score":0.10942590236663818}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7401708364486694},{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.710864782333374},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.5886011719703674},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5782439708709717},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5298454761505127},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5169350504875183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5164187550544739},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5079229474067688},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.49671870470046997},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4567881226539612},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.4558003544807434},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.443726122379303},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.42723312973976135},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.42272013425827026},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.4159095287322998},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.1804766058921814},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1531819999217987},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.10942590236663818},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3307772.3330167","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3307772.3330167","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Tenth ACM International Conference on Future Energy Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8299999833106995}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2025768430","https://openalex.org/W2035371892","https://openalex.org/W2064675550","https://openalex.org/W2071151783","https://openalex.org/W2122671960","https://openalex.org/W2135046866","https://openalex.org/W2949888546","https://openalex.org/W3007765569"],"related_works":["https://openalex.org/W2115072676","https://openalex.org/W4311212821","https://openalex.org/W2045727192","https://openalex.org/W2102065768","https://openalex.org/W4390752998","https://openalex.org/W1529660427","https://openalex.org/W3121106353","https://openalex.org/W2524120878","https://openalex.org/W2794343888","https://openalex.org/W2158578859"],"abstract_inverted_index":{"Load":[0],"forecasting":[1,23],"is":[2],"an":[3,92,122],"important":[4],"exercise":[5],"for":[6],"various":[7],"stakeholders":[8],"in":[9,76,91],"the":[10,31,41,46,58,88,98,103,111],"electricity":[11],"industry.":[12],"Electricity":[13],"consumption":[14,42],"profile":[15],"depends":[16],"on":[17],"several":[18],"factors":[19],"and":[20,116],"a":[21,74,107],"load":[22,69],"mechanism":[24],"must":[25],"be":[26],"able":[27],"to":[28,34,60,64],"use":[29,47],"all":[30],"relevant":[32],"information":[33],"predict":[35],"demand":[36],"accurately,":[37],"far":[38],"ahead":[39,68],"of":[40,48,51,87,102,110],"time.":[43],"We":[44],"prescribe":[45],"certain":[49],"family":[50],"recurrent":[52,100,117],"neural":[53,104],"network":[54,105],"models,":[55],"that":[56],"have":[57,73],"ability":[59],"handle":[61],"high-dimensional":[62],"features,":[63],"accurately":[65],"perform":[66],"p-step":[67],"forecasting.":[70],"These":[71],"models":[72],"built":[75],"feedforward":[77,115],"layer":[78],"which":[79],"effectively":[80],"performs":[81],"dimensionality":[82],"reduction":[83],"or":[84],"feature":[85],"selection":[86],"input":[89,112],"data":[90],"unsupervised":[93],"fashion.":[94],"In":[95],"other":[96],"words,":[97],"inner":[99],"layers":[101,118],"gets":[106],"sparse":[108],"representation":[109],"data.":[113],"The":[114],"are":[119],"trained":[120],"using":[121],"end-to-end":[123],"learning":[124],"framework.":[125]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}