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Warianty tytułu
Technika głębokiego uczenia się do prognozowania promieniowania słonecznego i prędkości wiatru na potrzeby dynamicznej analizy mikrosieci
Języki publikacji
Abstrakty
The key variables in the development and operation of wind and solar power systems are wind speed and solar radiation. The prediction of solar and wind energy parameters is important to alleviate the effects of power generation fluctuations. Consequently, it is essential to predict renewable energy sources like solar radiation and wind speed precisely. An artificial intelligence-based random forest method is recommended in this paper to estimate wind speed and solar radiation. The number of decision trees in the random forest model is suggested to be optimised using a novel coot algorithm (CA), and the effectiveness of the CA is evaluated to that of the currently used particle swarm optimisation (PSO) method. The best forecasting data are used in this work to develop a dynamic Microgrid (MG) in MATLAB/SIMULINK. A novel binary CA is proposed to control the MG to minimize the cost. The effect of the energy storage system is also investigated during the simulation of the MG.
Kluczowymi zmiennymi w rozwoju i działaniu systemów energii wiatrowej i słonecznej są prędkość wiatru i promieniowanie słoneczne. Prognozowanie parametrów energii słonecznej i wiatrowej jest ważne dla złagodzenia skutków wahań produkcji energii. W związku z tym niezbędne jest precyzyjne przewidywanie źródeł energii odnawialnej, takich jak promieniowanie słoneczne i prędkość wiatru. W tym artykule zaleca się metodę lasów losowych opartą na sztucznej inteligencji w celu oszacowania prędkości wiatru i promieniowania słonecznego. Sugeruje się optymalizację liczby drzew decyzyjnych w modelu losowego lasu przy użyciu nowego algorytmu łyski (CA), a skuteczność CA jest oceniana na podstawie obecnie stosowanej metody optymalizacji roju cząstek (PSO). W tej pracy wykorzystano najlepsze dane prognostyczne do opracowania dynamicznej mikrosieci (MG) w MATLAB/SIMULINK. Proponuje się nowy binarny CA do sterowania MG w celu zminimalizowania kosztów. Wpływ systemu magazynowania energii jest również badany podczas symulacji MG.
Wydawca
Czasopismo
Rocznik
Tom
Strony
162--170
Opis fizyczny
Bibliogr. 40 poz., rys., tab.
Twórcy
autor
- School of Engineering, Design and Built environment, Western Sydney University, Locked Bag 1797, NSW, 2751, Australia
autor
- Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, 15551, Al Ain, UAE
- Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, 15551, Al Ain, UAE
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-62214af7-6776-4161-af66-8e5acd7aff70