{"id":"https://openalex.org/W2904139120","doi":"https://doi.org/10.1145/3287921.3287936","title":"High Accuracy Forecasting with Limited Input Data","display_name":"High Accuracy Forecasting with Limited Input Data","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2904139120","doi":"https://doi.org/10.1145/3287921.3287936","mag":"2904139120"},"language":"en","primary_location":{"id":"doi:10.1145/3287921.3287936","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3287921.3287936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Ninth International Symposium on Information and Communication Technology  - SoICT 2018","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/A5034494547","display_name":"Elaine Zaunseder","orcid":"https://orcid.org/0000-0002-9642-9439"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Elaine Zaunseder","raw_affiliation_strings":["TU Berlin, Mathematics PricewaterhouseCoopers GmbH WPG, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TU Berlin, Mathematics PricewaterhouseCoopers GmbH WPG, Berlin, Germany","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109275724","display_name":"Larissa M\u00fcller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Larissa M\u00fcller","raw_affiliation_strings":["PricewaterhouseCoopers GmbH WPG, Hamburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PricewaterhouseCoopers GmbH WPG, Hamburg, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071457501","display_name":"Sven Blankenburg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sven Blankenburg","raw_affiliation_strings":["PricewaterhouseCoopers GmbH WPG, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PricewaterhouseCoopers GmbH WPG, Berlin, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2619,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.59539487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"61","last_page":"68"},"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.989300012588501,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6862115263938904},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6767463684082031},{"id":"https://openalex.org/keywords/renewable-energy","display_name":"Renewable energy","score":0.6264819502830505},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.49210184812545776},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.4862464666366577},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.4824827015399933},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4552858769893646},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4393233060836792},{"id":"https://openalex.org/keywords/feedforward-neural-network","display_name":"Feedforward neural network","score":0.42348411679267883},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.33356472849845886},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3185597062110901},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11266890168190002},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09428098797798157},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08489421010017395}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6862115263938904},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6767463684082031},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.6264819502830505},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.49210184812545776},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.4862464666366577},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.4824827015399933},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4552858769893646},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4393233060836792},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.42348411679267883},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.33356472849845886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3185597062110901},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11266890168190002},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09428098797798157},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08489421010017395},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3287921.3287936","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3287921.3287936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Ninth International Symposium on Information and Communication Technology  - SoICT 2018","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8299999833106995,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W417143071","https://openalex.org/W1522301498","https://openalex.org/W1586335931","https://openalex.org/W1964886660","https://openalex.org/W1974284277","https://openalex.org/W1995341919","https://openalex.org/W2015393497","https://openalex.org/W2021935701","https://openalex.org/W2024692966","https://openalex.org/W2036599383","https://openalex.org/W2036887984","https://openalex.org/W2039306928","https://openalex.org/W2051086873","https://openalex.org/W2064981081","https://openalex.org/W2068823081","https://openalex.org/W2068928057","https://openalex.org/W2074511771","https://openalex.org/W2076769467","https://openalex.org/W2079309933","https://openalex.org/W2089079906","https://openalex.org/W2090272943","https://openalex.org/W2093449717","https://openalex.org/W2113952909","https://openalex.org/W2130222651","https://openalex.org/W2146440428","https://openalex.org/W2149723649","https://openalex.org/W2153263933","https://openalex.org/W2166887182","https://openalex.org/W2288992240","https://openalex.org/W2296619665","https://openalex.org/W2343096921","https://openalex.org/W2607686132","https://openalex.org/W2782465264","https://openalex.org/W2786372250","https://openalex.org/W2950554777","https://openalex.org/W4236706032"],"related_works":["https://openalex.org/W2130522552","https://openalex.org/W2059163901","https://openalex.org/W1564141715","https://openalex.org/W4383370934","https://openalex.org/W1968787835","https://openalex.org/W2467981454","https://openalex.org/W3171420989","https://openalex.org/W2020847322","https://openalex.org/W2995861307","https://openalex.org/W1966177649"],"abstract_inverted_index":{"This":[0],"study":[1,45,58],"proposes":[2],"a":[3,120],"Feed":[4],"Forward":[5],"Neural":[6],"Net":[7],"(FFNN)":[8],"to":[9,38,48,71,86],"forecast":[10,39],"renewable":[11],"energy":[12,41],"generation":[13,29],"of":[14,61,66,83,106,118,126],"marine":[15],"wind":[16,40],"parks":[17],"located":[18],"in":[19,75],"Denmark.":[20],"The":[21,90],"neural":[22],"network":[23,85],"uses":[24],"historical":[25],"weather":[26,68],"and":[27,33,64],"power":[28],"data":[30,116,127],"for":[31],"training":[32,115],"applies":[34],"the":[35,44,59,62,67,72,84,88,99,102],"learned":[36],"pattern":[37],"production.":[42],"Furthermore,":[43],"shows":[46],"how":[47],"improve":[49,87],"prediction":[50],"quality":[51],"by":[52,98],"leveraging":[53],"specific":[54],"parameters.":[55],"Especially,":[56],"we":[57,79],"impact":[60],"distance":[63],"direction":[65],"station":[69],"related":[70],"production":[73],"site":[74],"detail.":[76],"In":[77],"addition,":[78],"examined":[80],"various":[81],"parameters":[82],"accuracy.":[89],"proposed":[91],"model":[92],"distinguishes":[93],"itself":[94],"from":[95],"other":[96],"models":[97],"fact":[100],"that":[101],"optimal":[103],"validation":[104],"accuracy":[105],"more":[107],"than":[108],"90":[109],"percent":[110],"can":[111],"be":[112],"reached":[113],"with":[114,128],"sets":[117],"only":[119],"limited":[121],"size,":[122],"here":[123],"two":[124],"months":[125],"hourly":[129],"resolution.":[130]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
