{"id":"https://openalex.org/W7204784192","doi":"https://doi.org/10.3390/electronics15173907","title":"Flexible Load Forecasting Method Based on Feature Decomposition and Adaptive Response Mechanism","display_name":"Flexible Load Forecasting Method Based on Feature Decomposition and Adaptive Response Mechanism","publication_year":2026,"publication_date":"2026-08-30","ids":{"openalex":"https://openalex.org/W7204784192","doi":"https://doi.org/10.3390/electronics15173907"},"language":"en","primary_location":{"id":"doi:10.3390/electronics15173907","doi":"https://doi.org/10.3390/electronics15173907","is_oa":true,"landing_page_url":"https://doi.org/10.3390/electronics15173907","pdf_url":null,"source":{"id":"https://openalex.org/S4210202905","display_name":"Electronics","issn_l":"2079-9292","issn":["2079-9292"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronics","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/electronics15173907","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024703187","display_name":"Haiqing Jia","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210088511","display_name":"Inner Mongolia Electric Power (China)","ror":"https://ror.org/0041szh84","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210088511"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiqing Jia","raw_affiliation_strings":["State Grid East Inner Mongolia Electric Power Co., Ltd., Hohhot 010090, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid East Inner Mongolia Electric Power Co., Ltd., Hohhot 010090, China","institution_ids":["https://openalex.org/I4210088511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061317374","display_name":"Jianbin Wang","orcid":"https://orcid.org/0000-0002-5323-3065","observed_orcids":["https://orcid.org/0000-0002-5323-3065"]},"institutions":[{"id":"https://openalex.org/I4210088511","display_name":"Inner Mongolia Electric Power (China)","ror":"https://ror.org/0041szh84","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210088511"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianbin Wang","raw_affiliation_strings":["State Grid East Inner Mongolia Electric Power Co., Ltd., Hohhot 010090, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid East Inner Mongolia Electric Power Co., Ltd., Hohhot 010090, China","institution_ids":["https://openalex.org/I4210088511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148783156","display_name":"Bohao Huang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bohao Huang","raw_affiliation_strings":["School of Electrical and Electronic Information, Xihua University, Chengdu 610039, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Information, Xihua University, Chengdu 610039, China","institution_ids":["https://openalex.org/I102345215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100725817","display_name":"Lei Xia","orcid":"https://orcid.org/0000-0003-3640-2063","observed_orcids":["https://orcid.org/0000-0003-3640-2063"]},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xia Lei","raw_affiliation_strings":["School of Electrical and Electronic Information, Xihua University, Chengdu 610039, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Information, Xihua University, Chengdu 610039, China","institution_ids":["https://openalex.org/I102345215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5130825340","display_name":"Jiawei Song","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Song","raw_affiliation_strings":["School of Electrical and Electronic Information, Xihua University, Chengdu 610039, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Information, Xihua University, Chengdu 610039, China","institution_ids":["https://openalex.org/I102345215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100725817"],"corresponding_institution_ids":["https://openalex.org/I102345215"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2896},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2896},"fwci":1.3169,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.73676692,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"15","issue":"17","first_page":"3907","last_page":"3907"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9965999722480774,"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":0.9965999722480774,"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/T10603","display_name":"Smart Grid Energy Management","score":0.0019000000320374966,"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":0.00019999999494757503,"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/short-term-load-forecasting","display_name":"short-term load forecasting","score":0.9950000047683716},{"id":"https://openalex.org/keywords/demand-response","display_name":"demand response","score":0.9940000176429749},{"id":"https://openalex.org/keywords/feature-decomposition","display_name":"feature decomposition","score":0.8149999976158142},{"id":"https://openalex.org/keywords/cnn-bilstm","display_name":"CNN-BiLSTM","score":0.7960000038146973},{"id":"https://openalex.org/keywords/electricity-price-signals","display_name":"electricity price signals","score":0.6179999709129333},{"id":"https://openalex.org/keywords/feature-weighting","display_name":"feature weighting","score":0.5170000195503235},{"id":"https://openalex.org/keywords/electricity-market","display_name":"electricity market","score":0.7839999794960022}],"concepts":[{"id":"https://openalex.org/C2779438525","wikidata":"https://www.wikidata.org/wiki/Q5255048","display_name":"Demand response","level":3,"score":0.8177000284194946},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.7343999743461609},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6855999827384949},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.6227999925613403},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.47769999504089355},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47450000047683716},{"id":"https://openalex.org/C2779370713","wikidata":"https://www.wikidata.org/wiki/Q357554","display_name":"Load management","level":2,"score":0.4253000020980835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4250999987125397},{"id":"https://openalex.org/C2983129042","wikidata":"https://www.wikidata.org/wiki/Q870344","display_name":"Electricity price","level":3,"score":0.4124999940395355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40369999408721924},{"id":"https://openalex.org/C19012869","wikidata":"https://www.wikidata.org/wiki/Q578372","display_name":"Response time","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.39160001277923584},{"id":"https://openalex.org/C146733006","wikidata":"https://www.wikidata.org/wiki/Q676081","display_name":"Electricity market","level":3,"score":0.3855000138282776},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C2777908891","wikidata":"https://www.wikidata.org/wiki/Q1806775","display_name":"Load profile","level":3,"score":0.3443000018596649},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33500000834465027},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C52970973","wikidata":"https://www.wikidata.org/wiki/Q2497134","display_name":"Adaptive system","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C2781104810","wikidata":"https://www.wikidata.org/wiki/Q23580049","display_name":"Electricity price forecasting","level":4,"score":0.2759999930858612},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2718999981880188},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.2612000107765198},{"id":"https://openalex.org/C2776003309","wikidata":"https://www.wikidata.org/wiki/Q1988072","display_name":"Adaptive algorithm","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/electronics15173907","doi":"https://doi.org/10.3390/electronics15173907","is_oa":true,"landing_page_url":"https://doi.org/10.3390/electronics15173907","pdf_url":null,"source":{"id":"https://openalex.org/S4210202905","display_name":"Electronics","issn_l":"2079-9292","issn":["2079-9292"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/electronics15173907","doi":"https://doi.org/10.3390/electronics15173907","is_oa":true,"landing_page_url":"https://doi.org/10.3390/electronics15173907","pdf_url":null,"source":{"id":"https://openalex.org/S4210202905","display_name":"Electronics","issn_l":"2079-9292","issn":["2079-9292"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronics","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.75,"id":"https://metadata.un.org/sdg/7"},{"display_name":"Industry, innovation and infrastructure","score":0.6773999929428101,"id":"https://metadata.un.org/sdg/9"},{"display_name":"Climate action","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/13"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1969596294","https://openalex.org/W2007221293","https://openalex.org/W2039346833","https://openalex.org/W2064675550","https://openalex.org/W2079735306","https://openalex.org/W2124092712","https://openalex.org/W2131534005","https://openalex.org/W2131774270","https://openalex.org/W2140311453","https://openalex.org/W2149723649","https://openalex.org/W2979600677","https://openalex.org/W3008244228","https://openalex.org/W3027737133","https://openalex.org/W4220897231","https://openalex.org/W4319069627","https://openalex.org/W4323840617","https://openalex.org/W4391731083","https://openalex.org/W4396792301","https://openalex.org/W4400350310","https://openalex.org/W4402082898","https://openalex.org/W4406334589","https://openalex.org/W4409423215","https://openalex.org/W4410951899","https://openalex.org/W4411078494","https://openalex.org/W4411348622","https://openalex.org/W4413057349","https://openalex.org/W7083169387","https://openalex.org/W7086905317"],"related_works":["https://openalex.org/W7161177316","https://openalex.org/W4226314867","https://openalex.org/W3198803998","https://openalex.org/W2960321303","https://openalex.org/W4380030797","https://openalex.org/W4417497128","https://openalex.org/W7167889826","https://openalex.org/W3154413738","https://openalex.org/W4393857938","https://openalex.org/W4319721681"],"abstract_inverted_index":{"With":[0],"the":[1,8,94,132,157,165,169],"ongoing":[2],"reform":[3],"of":[4,11,159],"electricity":[5,51,187],"markets":[6,188],"and":[7,46,54,63,86,112,150,176],"increasing":[9],"penetration":[10],"demand":[12],"response":[13,44,66,70,80],"(DR)":[14],"resources,":[15],"load":[16,26,55,96,184],"exhibits":[17],"stronger":[18],"price-related":[19,91],"uncertainty,":[20],"posing":[21],"significant":[22],"challenges":[23],"to":[24,59,76,87,106,124],"short-term":[25,183],"forecasting.":[27],"To":[28],"address":[29],"this":[30,32],"issue,":[31],"paper":[33],"proposes":[34],"a":[35,99,179],"DR-oriented":[36],"forecasting":[37,102,126,138,173,185],"framework":[38,171],"that":[39,131,140],"integrates":[40],"feature":[41],"decomposition,":[42],"adaptive":[43,69],"modeling,":[45],"deep":[47],"learning":[48],"techniques.":[49],"First,":[50],"price":[52],"signals":[53],"data":[56],"are":[57],"processed":[58],"identify":[60],"DR-related":[61],"features":[62],"extract":[64],"representative":[65],"characteristics.":[67],"An":[68],"mechanism":[71],"(ARM)":[72],"is":[73,104,122],"then":[74],"developed":[75],"learn":[77],"time-varying":[78],"statistical":[79],"characteristics":[81],"from":[82],"historical":[83],"load\u2013price":[84],"states":[85],"construct":[88],"an":[89,118,154],"explicit":[90],"correction":[92],"for":[93,182],"baseline":[95],"forecast,":[97],"while":[98],"hybrid":[100],"CNN-BiLSTM":[101],"model":[103],"constructed":[105],"capture":[107],"both":[108],"local":[109],"spatiotemporal":[110],"patterns":[111],"long-term":[113],"temporal":[114],"dependencies.":[115],"In":[116],"addition,":[117],"optimal":[119],"weighting":[120],"strategy":[121],"adopted":[123],"improve":[125],"performance.":[127],"Case":[128],"studies":[129],"demonstrate":[130],"proposed":[133,170],"method":[134],"consistently":[135],"outperforms":[136],"conventional":[137],"models":[139],"neglect":[141],"DR":[142,190],"effects,":[143],"yielding":[144],"notable":[145],"reductions":[146],"in":[147,156,186],"MAE,":[148],"RMSE,":[149],"MAPE,":[151],"together":[152],"with":[153,189],"increase":[155],"coefficient":[158],"determination":[160],"(R2).":[161],"By":[162],"effectively":[163],"quantifying":[164],"dynamic":[166],"price\u2013load":[167],"interaction,":[168],"improves":[172],"accuracy,":[174],"robustness,":[175],"interpretability,":[177],"providing":[178],"practical":[180],"solution":[181],"participation.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-10-03T13:16:28.481748","created_date":"2026-09-01T00:00:00"}