{"id":"https://openalex.org/W4415212992","doi":"https://doi.org/10.3390/en18205373","title":"Short-Term Probabilistic Prediction of Photovoltaic Power Based on Bidirectional Long Short-Term Memory with Temporal Convolutional Network","display_name":"Short-Term Probabilistic Prediction of Photovoltaic Power Based on Bidirectional Long Short-Term Memory with Temporal Convolutional Network","publication_year":2025,"publication_date":"2025-10-12","ids":{"openalex":"https://openalex.org/W4415212992","doi":"https://doi.org/10.3390/en18205373"},"language":"en","primary_location":{"id":"doi:10.3390/en18205373","doi":"https://doi.org/10.3390/en18205373","is_oa":true,"landing_page_url":"https://doi.org/10.3390/en18205373","pdf_url":"https://www.mdpi.com/1996-1073/18/20/5373/pdf?version=1760352918","source":{"id":"https://openalex.org/S198098182","display_name":"Energies","issn_l":"1996-1073","issn":["1996-1073"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","poland-140","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":"Energies","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1996-1073/18/20/5373/pdf?version=1760352918","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101785866","display_name":"Weibo Yuan","orcid":"https://orcid.org/0000-0003-2834-9899","observed_orcids":["https://orcid.org/0000-0003-2834-9899"]},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weibo Yuan","raw_affiliation_strings":["State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China","institution_ids":["https://openalex.org/I17442442"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079977172","display_name":"Jinjin Ding","orcid":"https://orcid.org/0000-0002-5339-4862","observed_orcids":["https://orcid.org/0000-0002-5339-4862"]},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinjin Ding","raw_affiliation_strings":["State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China","institution_ids":["https://openalex.org/I17442442"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Li Zhang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China","institution_ids":["https://openalex.org/I17442442"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114228448","display_name":"Jingyi Ni","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyi Ni","raw_affiliation_strings":["State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Anhui Electric Power Co., Ltd., Electric Power Research Institute, Hefei 230601, China","institution_ids":["https://openalex.org/I17442442"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100342656","display_name":"Qian Zhang","orcid":"https://orcid.org/0000-0001-8520-5854","observed_orcids":["https://orcid.org/0000-0001-8520-5854"]},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Zhang","raw_affiliation_strings":["School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China"],"raw_orcid":"https://orcid.org/0000-0001-8520-5854","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China","institution_ids":["https://openalex.org/I143868143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101785866"],"corresponding_institution_ids":["https://openalex.org/I17442442"],"apc_list":{"value":2600,"currency":"CHF","value_usd":3137},"apc_paid":{"value":2600,"currency":"CHF","value_usd":3137},"fwci":1.2371,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.81893339,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"18","issue":"20","first_page":"5373","last_page":"5373"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.008799999952316284,"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/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/solar-power-forecasting","display_name":"photovoltaic power forecasting","score":0.8119999766349792},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"probabilistic forecasting","score":0.843999981880188},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"quantile regression","score":0.7940000295639038},{"id":"https://openalex.org/keywords/bidirectional-lstm","display_name":"bidirectional LSTM","score":0.9409999847412109},{"id":"https://openalex.org/keywords/temporal-convolutional-network","display_name":"temporal convolutional network","score":0.9940000176429749},{"id":"https://openalex.org/keywords/prediction-intervals","display_name":"prediction intervals","score":0.6129999756813049},{"id":"https://openalex.org/keywords/confidence-intervals","display_name":"confidence intervals","score":0.6499999761581421},{"id":"https://openalex.org/keywords/huber-loss","display_name":"Huber loss","score":0.7950000166893005},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"kernel density estimation","score":0.9850000143051147},{"id":"https://openalex.org/keywords/meteorological-conditions","display_name":"weather conditions","score":0.574999988079071},{"id":"https://openalex.org/keywords/photovoltaic-power-uncertainty","display_name":"photovoltaic uncertainty","score":0.5910000205039978},{"id":"https://openalex.org/keywords/high-pv-penetration","display_name":"high PV penetration","score":0.7070000171661377}],"concepts":[{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.6844000220298767},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6399999856948853},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.6320000290870667},{"id":"https://openalex.org/C2776292839","wikidata":"https://www.wikidata.org/wiki/Q5179217","display_name":"Coverage probability","level":3,"score":0.6186000108718872},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5498999953269958},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.5105000138282776},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.46720001101493835},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.4262000024318695},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.4088999927043915},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.37869998812675476},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3783000111579895},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3506999909877777},{"id":"https://openalex.org/C103784038","wikidata":"https://www.wikidata.org/wiki/Q386228","display_name":"Cumulative distribution function","level":3,"score":0.3449000120162964},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C147947694","wikidata":"https://www.wikidata.org/wiki/Q837552","display_name":"Numerical weather prediction","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C140529851","wikidata":"https://www.wikidata.org/wiki/Q5160083","display_name":"Confidence and prediction bands","level":3,"score":0.27379998564720154},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27320000529289246},{"id":"https://openalex.org/C2777129202","wikidata":"https://www.wikidata.org/wiki/Q190107","display_name":"Weather station","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/en18205373","doi":"https://doi.org/10.3390/en18205373","is_oa":true,"landing_page_url":"https://doi.org/10.3390/en18205373","pdf_url":"https://www.mdpi.com/1996-1073/18/20/5373/pdf?version=1760352918","source":{"id":"https://openalex.org/S198098182","display_name":"Energies","issn_l":"1996-1073","issn":["1996-1073"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","poland-140","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":"Energies","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3c3daf08d51a474b8a66b5f63dc9aeba","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/3c3daf08d51a474b8a66b5f63dc9aeba","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Energies, Vol 18, Iss 20, p 5373 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/en18205373","doi":"https://doi.org/10.3390/en18205373","is_oa":true,"landing_page_url":"https://doi.org/10.3390/en18205373","pdf_url":"https://www.mdpi.com/1996-1073/18/20/5373/pdf?version=1760352918","source":{"id":"https://openalex.org/S198098182","display_name":"Energies","issn_l":"1996-1073","issn":["1996-1073"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","norway-1","poland-140","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":"Energies","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9853000044822693,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"},{"score":0.7207000255584717,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"},{"score":0.685699999332428,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415212992.pdf","grobid_xml":"https://content.openalex.org/works/W4415212992.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W3034070754","https://openalex.org/W3036677348","https://openalex.org/W3083590190","https://openalex.org/W3125561913","https://openalex.org/W3216775370","https://openalex.org/W4295916177","https://openalex.org/W4297973236","https://openalex.org/W4307822837","https://openalex.org/W4362635995","https://openalex.org/W4362636383","https://openalex.org/W4380053228","https://openalex.org/W4386597958","https://openalex.org/W4386917653","https://openalex.org/W4390738525","https://openalex.org/W4391254112","https://openalex.org/W4391970503","https://openalex.org/W4394809263"],"related_works":[],"abstract_inverted_index":{"To":[0,89],"mitigate":[1,261],"the":[2,16,43,63,80,91,101,111,151,180,187,207,228,235,238,244,255,284],"impact":[3],"of":[4,94,186,204,225,289],"photovoltaic":[5],"(PV)":[6],"power":[7,11,85,145,263,274,290],"generation":[8,264],"uncertainty":[9],"on":[10],"systems":[12],"and":[13,36,54,59,77,86,170,237,260,269,277,286],"accurately":[14],"depict":[15],"PV":[17,144,262,273,294],"output":[18,136],"range,":[19],"this":[20],"paper":[21],"proposes":[22],"a":[23,31,68,117,142,176],"quantile":[24,98],"regression":[25],"probabilistic":[26,275],"prediction":[27,97,139],"model":[28,123,189,256],"(TCN-QRBiLSTM)":[29],"integrating":[30],"Temporal":[32],"Convolutional":[33],"Network":[34],"(TCN)":[35],"Bidirectional":[37],"Long":[38],"Short-Term":[39],"Memory":[40],"(BiLSTM).":[41],"First,":[42],"historical":[44],"dataset":[45],"is":[46,70,104,133,199,214,220,234,243],"divided":[47],"into":[48],"three":[49,248],"weather":[50,65,192,249],"scenarios":[51],"(sunny,":[52],"cloudy,":[53],"rainy)":[55],"to":[56,72,124,135,158],"generate":[57,125],"training":[58],"test":[60],"samples":[61],"under":[62,175,190,246],"same":[64],"conditions.":[66,250],"Second,":[67],"TCN":[69],"used":[71],"extract":[73],"local":[74],"temporal":[75,82],"features,":[76],"BiLSTM":[78],"captures":[79],"bidirectional":[81],"dependencies":[83],"between":[84],"meteorological":[87],"data.":[88],"address":[90],"non-differentiable":[92],"issue":[93],"traditional":[95],"interval":[96,230],"loss":[99,113],"functions,":[100],"Huber":[102],"norm":[103],"introduced":[105],"as":[106,150],"an":[107],"approximate":[108],"replacement":[109],"for":[110,282],"original":[112],"function":[114],"by":[115],"constructing":[116],"differentiable":[118],"improved":[119],"Quantile":[120],"Regression":[121],"(QR)":[122],"confidence":[126,178],"intervals.":[127],"Finally,":[128],"Kernel":[129],"Density":[130],"Estimation":[131],"(KDE)":[132],"integrated":[134],"probability":[137],"density":[138],"results.":[140],"Taking":[141],"distributed":[143],"station":[146],"in":[147,271],"East":[148],"China":[149],"research":[152],"object,":[153],"using":[154],"data":[155],"from":[156],"July":[157],"September":[159],"2022":[160],"(15":[161],"min":[162],"resolution,":[163],"4128":[164],"samples),":[165],"comparative":[166,205,226],"verification":[167],"with":[168,292],"TCN-QRLSTM":[169],"QRBiLSTM":[171],"models":[172],"shows":[173],"that":[174,203,224,254],"90%":[177],"level,":[179],"Prediction":[181],"Interval":[182,209],"Coverage":[183],"Probability":[184],"(PICP)":[185],"proposed":[188],"sunny/cloudy/rainy":[191],"reaches":[193],"0.9901,":[194],"0.9553,":[195],"0.9674,":[196],"respectively,":[197,218],"which":[198,219],"0.56\u20133.85%":[200],"higher":[201],"than":[202,223],"models;":[206,227],"Percentage":[208],"Normalized":[210],"Average":[211],"Width":[212],"(PINAW)":[213],"0.1432,":[215],"0.1364,":[216],"0.1246,":[217],"1.35\u20136.49%":[221],"lower":[222],"comprehensive":[229],"evaluation":[231],"index":[232],"(I)":[233],"smallest;":[236],"Bayesian":[239],"Information":[240],"Criterion":[241],"(BIC)":[242],"lowest":[245],"all":[247],"The":[251],"results":[252],"demonstrate":[253],"can":[257],"effectively":[258],"quantify":[259],"uncertainty,":[265],"verifying":[266],"its":[267],"reliability":[268],"superiority":[270],"short-term":[272],"prediction,":[276],"it":[278],"has":[279],"practical":[280],"significance":[281],"ensuring":[283],"safe":[285],"economical":[287],"operation":[288],"grids":[291],"high":[293],"penetration.":[295]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2025-10-16T00:00:00"}
