{"id":"https://openalex.org/W7167052585","doi":"https://doi.org/10.1007/s44244-026-00029-5","title":"Photovoltaic power generation prediction method based on dynamic time warping and transformer","display_name":"Photovoltaic power generation prediction method based on dynamic time warping and transformer","publication_year":2026,"publication_date":"2026-07-02","ids":{"openalex":"https://openalex.org/W7167052585","doi":"https://doi.org/10.1007/s44244-026-00029-5"},"language":"en","primary_location":{"id":"doi:10.1007/s44244-026-00029-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44244-026-00029-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44244-026-00029-5.pdf","source":{"id":"https://openalex.org/S4387287974","display_name":"Industrial Artificial Intelligence","issn_l":"2731-667X","issn":["2731-667X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Industrial Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44244-026-00029-5.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139934728","display_name":"Chao Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I867306930","display_name":"Power Grid Corporation (India)","ror":"https://ror.org/0136vj189","country_code":"IN","type":"company","lineage":["https://openalex.org/I867306930"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chao Zheng","raw_affiliation_strings":["Yunnan Power Grid Co., Ltd, Kunming, 650051, Yunnan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan Power Grid Co., Ltd, Kunming, 650051, Yunnan, China","institution_ids":["https://openalex.org/I867306930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139886073","display_name":"Honghu Li","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Honghu Li","raw_affiliation_strings":["School of Information, Yunnan University, Kunming, 650504, Yunnan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information, Yunnan University, Kunming, 650504, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139878150","display_name":"Wei Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I867306930","display_name":"Power Grid Corporation (India)","ror":"https://ror.org/0136vj189","country_code":"IN","type":"company","lineage":["https://openalex.org/I867306930"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Wei Huang","raw_affiliation_strings":["Yunnan Power Grid Co., Ltd, Kunming, 650051, Yunnan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan Power Grid Co., Ltd, Kunming, 650051, Yunnan, China","institution_ids":["https://openalex.org/I867306930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139943254","display_name":"Xuehao He","orcid":null},"institutions":[{"id":"https://openalex.org/I867306930","display_name":"Power Grid Corporation (India)","ror":"https://ror.org/0136vj189","country_code":"IN","type":"company","lineage":["https://openalex.org/I867306930"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Xuehao He","raw_affiliation_strings":["Yunnan Power Grid Co., Ltd, Kunming, 650051, Yunnan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan Power Grid Co., Ltd, Kunming, 650051, Yunnan, China","institution_ids":["https://openalex.org/I867306930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5139935832","display_name":"Peng Li","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Li","raw_affiliation_strings":["School of Information, Yunnan University, Kunming, 650504, Yunnan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information, Yunnan University, Kunming, 650504, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5139935832"],"corresponding_institution_ids":["https://openalex.org/I189210763"],"apc_list":{"value":1795,"currency":"USD","value_usd":1795},"apc_paid":{"value":1795,"currency":"USD","value_usd":1795},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.84279879,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.6776999831199646,"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":0.6776999831199646,"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":0.11389999836683273,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.07779999822378159,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/dynamic-time-warping","display_name":"Dynamic time warping","score":0.8126999735832214},{"id":"https://openalex.org/keywords/photovoltaic-system","display_name":"Photovoltaic system","score":0.7143999934196472},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.6802999973297119},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6175000071525574},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.44530001282691956},{"id":"https://openalex.org/keywords/electricity-generation","display_name":"Electricity generation","score":0.385699987411499},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.38179999589920044},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.375900000333786},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.37130001187324524}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.8126999735832214},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.7143999934196472},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.6802999973297119},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6175000071525574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5008999705314636},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.44530001282691956},{"id":"https://openalex.org/C423512","wikidata":"https://www.wikidata.org/wiki/Q383973","display_name":"Electricity generation","level":3,"score":0.385699987411499},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.3287000060081482},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3188999891281128},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.27230000495910645},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26350000500679016},{"id":"https://openalex.org/C36139824","wikidata":"https://www.wikidata.org/wiki/Q1052007","display_name":"Maximum power point tracking","level":4,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s44244-026-00029-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44244-026-00029-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44244-026-00029-5.pdf","source":{"id":"https://openalex.org/S4387287974","display_name":"Industrial Artificial Intelligence","issn_l":"2731-667X","issn":["2731-667X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Industrial Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s44244-026-00029-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44244-026-00029-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44244-026-00029-5.pdf","source":{"id":"https://openalex.org/S4387287974","display_name":"Industrial Artificial Intelligence","issn_l":"2731-667X","issn":["2731-667X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Industrial Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.5803021788597107}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7167052585.pdf","grobid_xml":"https://content.openalex.org/works/W7167052585.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W2593382986","https://openalex.org/W2751698537","https://openalex.org/W2776400462","https://openalex.org/W2801596954","https://openalex.org/W2977155375","https://openalex.org/W3012038956","https://openalex.org/W3082644630","https://openalex.org/W3088638817","https://openalex.org/W3108854659","https://openalex.org/W3129734871","https://openalex.org/W3217258842","https://openalex.org/W4200048618","https://openalex.org/W4206624696","https://openalex.org/W4308197794","https://openalex.org/W4413444508","https://openalex.org/W6944002149"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"To":[1],"address":[2],"the":[3,11,34,39,61,75,81,94,107,111],"issue":[4],"of":[5,14,106],"low":[6],"prediction":[7,88,136],"accuracy":[8,120],"caused":[9],"by":[10],"strong":[12],"randomness":[13],"photovoltaic":[15],"(PV)":[16],"power":[17,24,58,86,96],"generation,":[18],"this":[19,115],"paper":[20,116],"proposes":[21],"a":[22,53,84],"PV":[23,57,85,95],"forecasting":[25],"method":[26,112],"based":[27,79],"on":[28,56,80],"Dynamic":[29],"Time":[30],"Warping":[31],"(DTW)":[32],"and":[33,41,72,131],"Transformer":[35,82],"model.":[36],"Firstly,":[37],"preprocess":[38],"data":[40,63],"use":[42],"pearson":[43],"correlation":[44],"coefficient":[45],"to":[46,92,122],"select":[47],"several":[48],"meteorological":[49],"factors":[50],"that":[51,110],"have":[52],"significant":[54],"impact":[55],"generation.":[59],"Secondly,":[60],"training":[62],"is":[64],"divided":[65],"into":[66],"three":[67,99],"weather":[68,101],"types:":[69],"sunny,":[70],"cloudy,":[71],"rainy":[73],"using":[74],"DTW":[76],"algorithm.":[77],"Finally,":[78],"model,":[83],"generation":[87,97],"model":[89],"was":[90],"established":[91],"predict":[93],"under":[98],"different":[100],"conditions.":[102],"The":[103],"verification":[104],"results":[105],"examples":[108],"show":[109],"proposed":[113],"in":[114],"achieves":[117],"higher":[118],"predictive":[119],"compared":[121],"Support":[123],"Vector":[124],"Regression":[125],"(SVR),":[126],"Long":[127],"Short-Term":[128],"Memory":[129],"(LSTM),":[130],"Gated":[132],"Recurrent":[133],"Units":[134],"(GRU)":[135],"methods.":[137]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2026-07-03T00:00:00"}
