{"id":"https://openalex.org/W3126233959","doi":"https://doi.org/10.1109/ias44978.2020.9334889","title":"Ultra-Short-Term Solar PV Power Forecasting Method Based on Frequency-Domain Decomposition and Deep Learning","display_name":"Ultra-Short-Term Solar PV Power Forecasting Method Based on Frequency-Domain Decomposition and Deep Learning","publication_year":2020,"publication_date":"2020-10-10","ids":{"openalex":"https://openalex.org/W3126233959","doi":"https://doi.org/10.1109/ias44978.2020.9334889","mag":"3126233959"},"language":"en","primary_location":{"id":"doi:10.1109/ias44978.2020.9334889","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ias44978.2020.9334889","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Industry Applications Society Annual Meeting","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/A5103109651","display_name":"Lin Hu","orcid":"https://orcid.org/0000-0002-7072-8887"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lin Hu","raw_affiliation_strings":["Department of Electrical Engineering, North China Electric Power University, Baoding, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, North China Electric Power University, Baoding, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100665356","display_name":"Zhao Zhen","orcid":"https://orcid.org/0000-0001-5406-6441"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Zhen","raw_affiliation_strings":["Department of Electrical Engineering, North China Electric Power University, Baoding, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, North China Electric Power University, Baoding, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455868","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0002-7332-9726"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Department of Electrical Engineering, North China Electric Power University, Baoding, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, North China Electric Power University, Baoding, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076577323","display_name":"Gang Qiu","orcid":null},"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":"Gang Qiu","raw_affiliation_strings":["Dispatch and Control Center, State Grid Xinjiang Electric Power Co., Ltd, Urumqi, China"],"affiliations":[{"raw_affiliation_string":"Dispatch and Control Center, State Grid Xinjiang Electric Power Co., Ltd, Urumqi, China","institution_ids":["https://openalex.org/I4210088511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100345977","display_name":"Yu Li","orcid":"https://orcid.org/0009-0008-4964-5869"},"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":"Yu Li","raw_affiliation_strings":["Dispatch and Control Center, State Grid Xinjiang Electric Power Co., Ltd, Urumqi, China"],"affiliations":[{"raw_affiliation_string":"Dispatch and Control Center, State Grid Xinjiang Electric Power Co., Ltd, Urumqi, China","institution_ids":["https://openalex.org/I4210088511"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088560163","display_name":"Miadreza Shafie\u2010khah","orcid":"https://orcid.org/0000-0003-1691-5355"},"institutions":[{"id":"https://openalex.org/I79765343","display_name":"University of Vaasa","ror":"https://ror.org/03769b225","country_code":"FI","type":"education","lineage":["https://openalex.org/I79765343"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Miadreza Shafie-khah","raw_affiliation_strings":["School of Technology and Innovations, University of Vaasa, Vaasa, Finland"],"affiliations":[{"raw_affiliation_string":"School of Technology and Innovations, University of Vaasa, Vaasa, Finland","institution_ids":["https://openalex.org/I79765343"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065382518","display_name":"Jo\u00e3o P. S. Catal\u00e0o","orcid":"https://orcid.org/0000-0002-2105-3051"},"institutions":[{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]},{"id":"https://openalex.org/I4210166615","display_name":"INESC TEC","ror":"https://ror.org/05fa8ka61","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210125590","https://openalex.org/I4210166615"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Joao P. S. Catalao","raw_affiliation_strings":["Faculty of Engineering of the University of Porto and INESC TEC, Porto, Portugal"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering of the University of Porto and INESC TEC, Porto, Portugal","institution_ids":["https://openalex.org/I4210166615","https://openalex.org/I182534213"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5103109651"],"corresponding_institution_ids":["https://openalex.org/I153473198"],"apc_list":null,"apc_paid":null,"fwci":0.6628,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.76993997,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9991000294685364,"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":0.9983999729156494,"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/photovoltaic-system","display_name":"Photovoltaic system","score":0.651397705078125},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.6338908672332764},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5368958115577698},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5216817855834961},{"id":"https://openalex.org/keywords/sine-wave","display_name":"Sine wave","score":0.5047594308853149},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4377533197402954},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4354499280452728},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4158198833465576},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2505965530872345},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1867000162601471},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.1092887818813324}],"concepts":[{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.651397705078125},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.6338908672332764},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5368958115577698},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5216817855834961},{"id":"https://openalex.org/C66907618","wikidata":"https://www.wikidata.org/wiki/Q207527","display_name":"Sine wave","level":3,"score":0.5047594308853149},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4377533197402954},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4354499280452728},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4158198833465576},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2505965530872345},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1867000162601471},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1092887818813324},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ias44978.2020.9334889","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ias44978.2020.9334889","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Industry Applications Society Annual Meeting","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.5899999737739563}],"awards":[],"funders":[{"id":"https://openalex.org/F4320326707","display_name":"State Grid Corporation of China","ror":"https://ror.org/05twwhs70"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W2001535629","https://openalex.org/W2115294291","https://openalex.org/W2173259274","https://openalex.org/W2594044533","https://openalex.org/W2739874019","https://openalex.org/W2797889343","https://openalex.org/W2806285575","https://openalex.org/W2806443828","https://openalex.org/W2807818015","https://openalex.org/W2809311023","https://openalex.org/W2887743720","https://openalex.org/W2888449021","https://openalex.org/W2891460982","https://openalex.org/W2892164648","https://openalex.org/W2897143050","https://openalex.org/W2906239552","https://openalex.org/W2920873814","https://openalex.org/W2948490758","https://openalex.org/W2948835715","https://openalex.org/W2950348622","https://openalex.org/W2952250445","https://openalex.org/W2964598435","https://openalex.org/W2967537049","https://openalex.org/W2990572506","https://openalex.org/W2999862096","https://openalex.org/W3001995063","https://openalex.org/W3007599907","https://openalex.org/W3007976404","https://openalex.org/W3011353627","https://openalex.org/W3011386070","https://openalex.org/W3014900786","https://openalex.org/W3015024348","https://openalex.org/W3016208458","https://openalex.org/W3024869055","https://openalex.org/W3038037083","https://openalex.org/W4241485379","https://openalex.org/W6753554545","https://openalex.org/W6770576186"],"related_works":["https://openalex.org/W2386968573","https://openalex.org/W2395064349","https://openalex.org/W2034374297","https://openalex.org/W2766130412","https://openalex.org/W2357256365","https://openalex.org/W2382628689","https://openalex.org/W2526730640","https://openalex.org/W2351171996","https://openalex.org/W2348502264","https://openalex.org/W2983370139"],"abstract_inverted_index":{"Ultra-short-term":[0],"photovoltaic":[1,29],"(PV)":[2],"power":[3,11,19,30,53],"forecasting":[4,39,49,149,160,195],"can":[5],"support":[6],"the":[7,14,28,36,63,79,85,88,92,99,102,105,116,124,157,163,167,172,176,191],"real-time":[8],"dispatching":[9],"of":[10,17,41,51,67,101,162,174],"grid":[12],"and":[13,59,65,91,110,121,144,147,166,183],"optimal":[15,56,125],"operation":[16],"PV":[18,42,52],"station":[20],"itself.":[21],"However,":[22],"due":[23],"to":[24,140],"various":[25],"meteorological":[26],"factors,":[27],"has":[31],"great":[32],"fluctuations.":[33],"To":[34],"improve":[35],"refined":[37],"ultra-short-term":[38,48],"technology":[40],"power,":[43],"this":[44],"paper":[45,158],"proposes":[46],"an":[47],"model":[50,165,170,193],"based":[54],"on":[55],"frequency-domain":[57],"decomposition":[58,89,130],"deep":[60],"learning.":[61],"First,":[62],"amplitude":[64],"phase":[66],"each":[68],"frequency":[69,80,126],"sine":[70],"wave":[71],"is":[72,83,95,113,138,151],"obtained":[73,152],"by":[74,153],"fast":[75],"Fourier":[76],"decomposition.":[77],"As":[78],"demarcation":[81,127],"point":[82],"different,":[84],"correlation":[86,106,117],"between":[87,107,118],"component":[90,143],"original":[93],"data":[94,112],"analyzed.":[96],"By":[97],"minimizing":[98],"square":[100],"difference":[103],"that":[104,190],"low-frequency":[108,142],"components":[109,120,131],"raw":[111,122],"subtracted":[114],"from":[115],"high-frequency":[119,145],"data,":[123],"points":[128],"for":[129],"are":[132],"obtained.":[133],"Then":[134],"convolutional":[135],"neural":[136],"network":[137],"used":[139],"predict":[141],"component,":[146],"final":[148],"result":[150],"addition":[154],"reconstruction.":[155],"Finally,":[156],"compares":[159],"results":[161,187],"proposed":[164,192],"non-spectrum":[168],"analysis":[169],"in":[171],"case":[173],"predicting":[175],"1":[177],"hour,":[178],"2":[179],"hours,":[180,182],"3":[181],"4":[184],"hours.":[185],"The":[186],"fully":[188],"show":[189],"improves":[194],"accuracy.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}