Descrição Estatística Do Ciclo Diário Do Vento Nos Primeiros 100 Metros De Altura Da C.L.P. Na Localidade De Colonia Eulacio, Uruguai

Lucas, Everton de Almeida; Arce, Alejandro Mauricio Gutiérrez; Moraes, Marcelo Romero de; Boezio, Gabriel Cazes; Ottieri, José Cataldo

doi:10.5902/2179460x20308

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Regarding the computational procedure, we adopted a computational-intelligence model using a Multilayer Perceptron ANN with Levenberg–Marquardt Backpropagation and a training algorithm for short-term wind speed forecasting (1, 3, 6, 9, and 12 h) in Colonia Eulacio, Soriano Department, Uruguay. The mean wind diurnal cycle in different seasons for this location was described by Lucas et al. (2016), whose analysis employed the same data used in the present study.…”

Section: Methodsmentioning

confidence: 99%

“…As previously mentioned, the measuring station used for this study is located in the southwestern region of Uruguay (Colonia Eulacio, Soriano Department) and is composed of a triangular tower 100.8 m in height and 0.45 m wide. According to Datum WGS84, it is located at 33 o 16 ′S, 57 o 31 ′W (Lucas et al., 2016). The altitude of the installation location is approximately 100 m, and the location is surrounded by fields with plains; thus, it is characterised by non-complex terrain.…”

Section: Methodsmentioning

confidence: 99%

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Short-term wind speed forecasting in Uruguay using computational intelligence

Zucatelli

Nascimento

Aylas

et al. 2019

Heliyon

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Short-term wind speed forecasting for Colonia Eulacio, Soriano Department, Uruguay, is performed by applying an artificial neural network (ANN) technique to the hourly time series representative of the site. To train the ANN and validate the technique, data for one year are collected by one tower, with anemometers installed at heights of 101.8, 81.8, 25.7, and 10.0 m. Different ANN configurations are applied for each site and height; then, a quantitative analysis is conducted, and the statistical results are evaluated to select the configuration that best predicts the real data. This method has lower computational costs than other techniques, such as numerical modelling. For integrating wind power into existing grid systems, accurate short-term wind speed forecasting is fundamental. Therefore, the proposed short-term wind speed forecasting method is an important scientific contribution for reliable large-scale wind power forecasting and integration in Uruguay. The results of the short-term wind speed forecasting showed good accuracy at all the anemometer heights tested, suggesting that the method is a powerful tool that can help the Administración Nacional de Usinas y Transmissiones Eléctricas manage the national energy supply.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Short-term wind speed forecasting in Uruguay using computational intelligence

Zucatelli

Nascimento

Aylas

et al. 2019

Heliyon

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“…As for Colonia Eulacio Tower in Uruguay, according to datum WGS84, it is located at 33 o 16′ S, 57 o 31′ W [25]. The altitude of the installation location (see Figure 3) is approximately 100.0 m, and the location is surrounded by fields with plains; thus, it is characterized by noncomplex terrain.…”

Section: Methodsmentioning

confidence: 99%

Study of the Wind Speed Forecasting Applying Computational Intelligence

Zucatelli¹,

Nascimento²,

Santos³

et al. 2021

Aerodynamics

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The conventional sources of energy such as oil, natural gas, coal, or nuclear are finite and generate environmental pollution. Alternatively, renewable energy source like wind is clean and abundantly available in nature. Wind power has a huge potential of becoming a major source of renewable energy for this modern world. It is a clean, emission-free power generation technology. Wind energy has been experiencing very rapid growth in Brazil and in Uruguay; therefore, it's a promising industry in these countries. Thus, this rapid expansion can bring several regional benefits and contribute to sustainable development, especially in places with low economic development. Therefore, the scope of this chapter is to estimate short-term wind speed forecasting applying computational intelligence, by recurrent neural networks (RNN), using anemometers data collected by an anemometric tower at a height of 100.0 m in Brazil (tropical region) and 101.8 m in Uruguay (subtropical region), both Latin American countries. The results of this study are compared with wind speed prediction results from the literature. In one of the cases investigated, this study proved to be more appropriate when analyzing evaluation metrics (error and regression) of the prediction results obtained by the proposed model.

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Análise de perfil logarítmico do vento por meio de equações diferenciais: um estudo no município de São Bento, Maranhão

Melo,

Aguiar,

Silva

et al. 2024

Cuad. Ed. Desar.

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A aplicação de equações diferenciais na análise do perfil logarítmico do vento, se faz necessária para se analisar aspectos críticos na compreensão de fluxos atmosféricos em camadas limites. Para que isso ocorra, a apropriação da Teoria da Similaridade e das equações de Navier-Stokes são importantes, pois são utilizadas para derivar e modelar o comportamento do vento em relação à altura. O objetivo é apresentar uma modelagem matemática do perfil logarítmico do vento de maneiras que nos permita investigar se é viável a produção de energia eólica na cidade de São Bento/MA. Para isso, explora-se os métodos analíticos e numéricos para resolver as equações diferenciais que descrevem esse fenômeno. O perfil do vento foi analisado a partir dos dados coletados na estação meteorológica do Instituto Nacional de Meteorologia (INMET) da cidade de São Bento/MA, assim como, pela validação dos modelos teóricos utilizados, no período de 17/04 a 21/04/2024, período em que se observou as maiores velocidades médias. A pesquisa revelou que, ainda existem desafios a serem transpostos, assim como limitações inerentes aos modelos diferenciais usados. Pois, os dados analisados não indicam a possibilidade de geração de energia eólica nessa cidade maranhense. No entanto, conclui-se que embora os resultados não tenham sido satisfatórios, mesmo assim se tornam promissores para futuras investigações.

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Descrição Estatística Do Ciclo Diário Do Vento Nos Primeiros 100 Metros De Altura Da C.L.P. Na Localidade De Colonia Eulacio, Uruguai

Cited by 3 publications

References 3 publications

Short-term wind speed forecasting in Uruguay using computational intelligence

Short-term wind speed forecasting in Uruguay using computational intelligence

Study of the Wind Speed Forecasting Applying Computational Intelligence

Análise de perfil logarítmico do vento por meio de equações diferenciais: um estudo no município de São Bento, Maranhão

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