{"id":"https://openalex.org/W4360584211","doi":"https://doi.org/10.1109/isgt51731.2023.10066413","title":"Short-Term Load Forecasting using Conditionally Restricted Boltzman Machine Optimized by Modified Grasshopper Optimization Algorithm","display_name":"Short-Term Load Forecasting using Conditionally Restricted Boltzman Machine Optimized by Modified Grasshopper Optimization Algorithm","publication_year":2023,"publication_date":"2023-01-16","ids":{"openalex":"https://openalex.org/W4360584211","doi":"https://doi.org/10.1109/isgt51731.2023.10066413"},"language":"en","primary_location":{"id":"doi:10.1109/isgt51731.2023.10066413","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isgt51731.2023.10066413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Power &amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)","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/A5103054219","display_name":"M. Zulfiqar","orcid":"https://orcid.org/0000-0002-1624-3045"},"institutions":[{"id":"https://openalex.org/I142732210","display_name":"University of Engineering and Technology Lahore","ror":"https://ror.org/0051w2v06","country_code":"PK","type":"education","lineage":["https://openalex.org/I142732210"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Muhammad Zulfiqar","raw_affiliation_strings":["University of Engineering and Technology,Department of Electrical Engineering,Lahore,Pakistan","Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Engineering and Technology,Department of Electrical Engineering,Lahore,Pakistan","institution_ids":["https://openalex.org/I142732210"]},{"raw_affiliation_string":"Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan","institution_ids":["https://openalex.org/I142732210"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020213187","display_name":"Muhammad Babar Rasheed","orcid":"https://orcid.org/0000-0002-9911-0693"},"institutions":[{"id":"https://openalex.org/I189268942","display_name":"Universidad de Alcal\u00e1","ror":"https://ror.org/04pmn0e78","country_code":"ES","type":"education","lineage":["https://openalex.org/I189268942"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Muhammad Babar Rasheed","raw_affiliation_strings":["Universidad de Alcal&#x00E1;, Escuela Polit&#x00E9;cnica Superior, ISG,Alcal&#x00E1; de Henares,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad de Alcal&#x00E1;, Escuela Polit&#x00E9;cnica Superior, ISG,Alcal&#x00E1; de Henares,Spain","institution_ids":["https://openalex.org/I189268942"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018038621","display_name":"Mar\u00eda D. R\u2010Moreno","orcid":"https://orcid.org/0000-0002-7024-0427"},"institutions":[{"id":"https://openalex.org/I189268942","display_name":"Universidad de Alcal\u00e1","ror":"https://ror.org/04pmn0e78","country_code":"ES","type":"education","lineage":["https://openalex.org/I189268942"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Mar\u00eda D. R-Moreno","raw_affiliation_strings":["Universidad de Alcal&#x00E1;, Escuela Polit&#x00E9;cnica Superior, ISG,Alcal&#x00E1; de Henares,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad de Alcal&#x00E1;, Escuela Polit&#x00E9;cnica Superior, ISG,Alcal&#x00E1; de Henares,Spain","institution_ids":["https://openalex.org/I189268942"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3681,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.57073206,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"10","issue":null,"first_page":"1","last_page":"5"},"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":0.9825999736785889,"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":0.9807000160217285,"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/computer-science","display_name":"Computer science","score":0.7014858722686768},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5836388468742371},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5649328827857971},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5090138912200928},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.4733866751194},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4579313099384308},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4418233036994934},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41647446155548096},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3444540202617645},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15106093883514404}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7014858722686768},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5836388468742371},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5649328827857971},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5090138912200928},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.4733866751194},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4579313099384308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4418233036994934},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41647446155548096},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3444540202617645},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15106093883514404},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isgt51731.2023.10066413","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isgt51731.2023.10066413","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Power &amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"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":14,"referenced_works":["https://openalex.org/W1490308602","https://openalex.org/W2017588182","https://openalex.org/W2068438324","https://openalex.org/W2140095548","https://openalex.org/W2275865104","https://openalex.org/W2510931397","https://openalex.org/W2521449822","https://openalex.org/W2597866042","https://openalex.org/W2601171548","https://openalex.org/W2622770036","https://openalex.org/W2783069604","https://openalex.org/W2810878542","https://openalex.org/W2888909529","https://openalex.org/W2919979744"],"related_works":["https://openalex.org/W2140798747","https://openalex.org/W2948169060","https://openalex.org/W2730112582","https://openalex.org/W2110696645","https://openalex.org/W2358580169","https://openalex.org/W2111347279","https://openalex.org/W4399426197","https://openalex.org/W2487211728","https://openalex.org/W2378096925","https://openalex.org/W2485376993"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,112],"novel":[4],"hybrid":[5],"model":[6,107,135,158,214,219,224,228,233],"by":[7,192,195,199],"integrating":[8],"data":[9],"pre-processing,":[10],"feature":[11],"extraction":[12],"(FX),":[13],"and":[14,23,40,77,88,130,177,183,197,231],"forecasting":[15,106],"&":[16],"optimization":[17,53],"modules":[18],"to":[19,43,68,98,114],"improve":[20],"the":[21,27,38,45,50,57,62,70,74,79,86,102,103,123,133,139,144,153,156,161,207,212,217,222],"accuracy":[22,87,129],"convergence":[24,89,131],"rate.":[25],"Where,":[26],"radial":[28],"basis":[29],"kernel-principal":[30],"component":[31],"analysis":[32],"(RB-KPCA)":[33],"based":[34],"FX":[35],"algorithm":[36,54],"eliminates":[37],"redundant":[39],"irrelevant":[41],"features":[42],"ensure":[44],"high":[46],"computational":[47],"efficiency.":[48],"While,":[49],"modified":[51],"grasshopper":[52],"(mGOA)":[55],"optimizes":[56],"appropriate":[58],"control":[59],"parameters":[60],"of":[61,127,143,155,203,211],"conditionally":[63],"restricted":[64],"Boltzmann":[65],"machine":[66],"(CRBM)":[67],"avoid":[69],"solution":[71],"trapping":[72],"into":[73],"local":[75],"optimum":[76],"returns":[78],"results":[80,150],"with":[81],"improved":[82],"accuracy.":[83,205],"Furthermore,":[84,206],"since":[85],"rate":[90],"are":[91,96],"two":[92],"contradictory":[93],"objectives,":[94],"which":[95],"difficult":[97],"achieve,":[99],"simultaneously.":[100],"However,":[101],"proposed":[104,134,157,213],"FX-CRBM-mGOA":[105],"is":[108,136,159,167,173,181,185,215,220,225,229,234],"designed":[109],"in":[110,125,201],"such":[111],"way":[113],"simultaneously":[115],"achieve":[116],"these":[117],"relatively":[118],"independent":[119],"objectives.":[120],"To":[121],"evaluate":[122],"performance":[124],"terms":[126,202],"MAPE,":[128],"rate,":[132],"implemented":[137],"on":[138],"publically":[140],"available":[141],"dataset":[142],"Dayton":[145],"power":[146],"grid,":[147],"USA.":[148],"The":[149,187],"show":[151],"that":[152],"MAPE":[154],"0.4525%,":[160],"information-based":[162],"artificial":[163],"neural":[164],"network":[165],"(MI-ANN)":[166],"2.4202%,":[168],"long":[169],"short-term":[170],"memory":[171],"(LSTM)":[172],"3.231%,":[174],"ANN-based":[175],"accurate":[176],"fast":[178],"converging":[179],"(AFC-ANN)":[180],"2.452%,":[182],"Bi-Level":[184],"2.123%.":[186],"devised":[188],"framework":[189],"outperforms":[190],"MI-ANN":[191,227],"3.2%,":[193],"Bi-level":[194,223],"2.3%,":[196],"AFC-ANN":[198,218],"3.1%":[200],"forecast":[204],"average":[208],"execution":[209],"time":[210],"49s,":[216],"69s,":[221],"52s,":[226],"78s,":[230],"LSTM":[232],"54s.":[235]},"counts_by_year":[{"year":2024,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
