Komparativna analiza dubokih neuralnih mreža i algoritama gradijentnog pojačanja u dugoročnom predviđanju snage vetra
Sažetak
Ključni korak ka održivoj budućnosti je integracija obnovljivih izvora energije u elektroenergetsku mrežu. Energija vetra je značajna zbog svoje široke dostupnosti i minimalnog uticaja na životnu sredinu. Ovaj rad predstavlja komparativnu analizu algoritama rekurentnih neuralnih mreža (RNN) i algoritama gradijentnog pojačanja (GBM) primenjenih na vremenske serije podataka za regresioni problem procene aktivne snage koju proizvodi vetropark (WF). GBM algoritmi kombinuju prednosti nekoliko modela mašinskog učenja (stabla odlučivanja, slučajne šume, itd.) kako bi proizveli moćan model predikcije. Pored postojećih konvencionalnih RNN, članak se bavi dugoročnim memorijskim jedinicama (LSTM) i rekurentnim jedinicama sa kapijama (GRU) kao najmodernijim modelima za predikciju vremenskih serija. Sveobuhvatna analiza je sprovedena na velikom skupu podataka o proizvodnji energije vetra.
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