Deep learning technique for forecasting solar radiation and wind speed for dynamic microgrid analysis

• Autorzy: Islam Md Mainul , Shareef Hussain , Al Hassan Eslam Salah Fayez

Identyfikatory

DOI

10.15199/48.2023.04.27

Warianty tytułu

PL

Technika głębokiego uczenia się do prognozowania promieniowania słonecznego i prędkości wiatru na potrzeby dynamicznej analizy mikrosieci

• Języki publikacji: EN

Abstrakty

EN

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.

PL

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.

Słowa kluczowe

EN

solar power; wind power; random forest method; coot algorithm; microgrid; forecasting

PL

energia słoneczna; energia wiatrowa; prognozowanie; głębokie uczenie

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 4
• Strony: 162--170
• Opis fizyczny: Bibliogr. 40 poz., rys., tab.

Twórcy

autor

Islam Md Mainul

• School of Engineering, Design and Built environment, Western Sydney University, Locked Bag 1797, NSW, 2751, Australia

• https://orcid.org/0000-0002-8079-2065

autor

Shareef Hussain

• Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, 15551, Al Ain, UAE

• https://orcid.org/0000-0001-7708-6904

autor

Al Hassan Eslam Salah Fayez

• Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, 15551, Al Ain, UAE

• https://orcid.org/0000-0002-4909-5106

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