Análises de Componente Principal e de Agrupamento para estudo de ventos para a geração de energia eólica na região do Ceará, Paraíba, Pernambuco e Rio Grande do Norte, Brasil

Lima, Francisco José Lopes de; Amanajás, Jonathan Castro; Guedes, Roni Valter de Souza; Silva, Emerson Mariano da

doi:10.4136/ambi-agua.147

Cited by 8 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To select the number of PCA components, the Kaiser criterion was used (Kaiser, 1958); in which selects all principal components with eigenvalues equal to or greater than 1. Once the components were selected in the PCA, cluster analysis was performed according to the Ward method, to form groups of homogeneous individuals (Lima et al, 2010). According to Nakamura et al (2013), the Ward method is the most widely used method for cluster analysis because of the strong statistical appeal involved in the calculation process; this process uses the analysis of variance to perform the clustering of individuals, which are the fields R. Bras.…”

Section: Methodsmentioning

confidence: 99%

No-tillage system participatory quality index

Telles

Righetto

Lourenço

et al. 2020

Rev. bras. eng. agríc. ambient.

View full text Add to dashboard Cite

The no-tillage system participatory quality index aims to evaluate the quality and efficiency of soil management under no-tillage systems and consists of a weighted sum of eight indicators: intensity of crop rotation, diversity of crop rotation, persistence of crop residues in the soil surface, frequency of soil tillage, use of agricultural terraces, evaluation of soil conservation, balance of soil fertilization and time of adoption of the no-tillage system. The aim of this study was to assess the extent to which these indicators correlate with the no-tillage system participatory quality index and to characterize the farmers who participated in the research. The data used were provided by ITAIPU Binacional for the indicators of the no-tillage system participatory quality index II. Descriptive analyses were performed, and the Pearson correlation coefficient between the index and each indicator was calculated. To assess the relationship between the indicators and the farmers’ behavior toward the indicators, principal component analysis and cluster analysis were performed. Although all correlations are significant at p-value ≤ 0.05, some correlations are weak, indicating a need for improvement of the index. The principal component analysis identified three principal components, which explained 66% of the variability of the data, and the cluster analysis separated the 121 farmers into five groups. It was verified that the no-tillage system participatory quality index II has some limitations and should therefore be reevaluated to increase its efficiency as an indicator of the quality of the no-tillage system.

show abstract

Section: Methodsmentioning

confidence: 99%

No-tillage system participatory quality index

Telles

Righetto

Lourenço

et al. 2020

Rev. bras. eng. agríc. ambient.

View full text Add to dashboard Cite

show abstract

“…A análise de agrupamento (AA) é uma técnica estatística de análise multivariada tem como objetivo classificar os indivíduos de uma população que são conhecidos por suas características, em grupos que sejam homogêneos intragrupos e heterogêneos intergrupos (Silva et al, 2013;Lima et al, 2010). As técnicas de agrupamento diminuem a subjetividade, pois quantificam a similaridade ou dissimilaridade entre os indivíduos.…”

Section: Análise De Agrupamento (Aa)unclassified

Análise de Agrupamento na Otimização de Futuras Aplicações de Modelagem Numérica na Estimativa e Previsão de Recurso Eólico (Research for Optimization of Numerical Modeling Applied to Estimate and Forecast Wind Resource)

Pinto

Lima

Martins

et al. 2017

RBGF

View full text Add to dashboard Cite

Artigo recebido em 06/05/2017 e aceito em 11/10/2017 R E S U M O A confiabilidade da estimativa e da variabilidade do vento num local ou região de interesse é essencial, principalmente para empreendimentos de energia eólica. Atualmente o mapeamento de áreas com elevado potencial para a exploração comercial pode ser realizado com o uso de modelos atmosféricos que demandam um investimento relativamente baixo. Porém para realizar as simulações com modelos atmosféricos de mesoescala são necessários determinar os domínios (grades) do modelo que podem variar de alguns poucos km a dezenas de km. Com o objetivo de auxiliar na determinação destes domínios será utilizada a técnica de análise de agrupamento (Cluster Analysis) nas séries de dados observados nas estações meteorológicas automáticas do Instituto Nacional de Meteorologia (INMET) para o nordeste do Brasil. Esta classificação consiste em determinar o nível de similaridade ou dissimilaridade entre indivíduos aplicando uma função de agrupamento a uma determinada variável. Está análise tem como principal objetivo determinar regiões com padrões de intensidade do vento semelhante dentro do domínio do estudo, e assim facilitar a localização das grades nas simulações dos modelos, evitando grades que possuam pontos com diferentes padrões de vento. Foram utilizados dados horários de velocidade do vento a 10 m de altura de 121 estações meteorológicas automáticas, no período de janeiro de 2005 a dezembro de 2013. Os resultados obtidos confirmaram que há regiões com diferentes padrões de vento no nordeste, que necessitam configurações de grade distintas e parametrizações físicas adequadas aos fenômenos meteorológicos observados em cada região. Palavras Chaves: Estimativa do vento, Energia eólica, Análise de agrupamento. Research for Optimization of Numerical Modeling Applied to Estimate and ForecastWind Resource A B S T R A C TThe dependability of estimate and variability of the wind in a place or region of interest is essential, primarily for wind power projects. Currently the mapping of areas with high potential for commercial exploitation is carried out with the use of atmospheric models that require a relatively low investment. However to make simulations with mesoscale atmospheric models are required to identify model domains (grids) that can range from few kilometers to tens of kilometers. The goal is determine this domains cluster, the analysis will be made to the observed data from automated weather stations of the National Institute of Meteorology (INMET) to Northeast Brazil. This classification determine the level of similarity or dissimilarity between individuals applying a clustering function to a given variable. This analysis has as main goal, determine regions with intensity patterns of similar wind within domain of the study, and thus facilitate the location of the grids in the simulations of the models, avoiding spots gratings with patterns different of wind. Hourly data were used for wind speed at 10 m height of 121 automatic weather stations, from January 2...

show abstract

“…Estudos realizados por Singh et al (2004), Shrestha e Kazama (2007), Bricker e Jones (1995), Fan et al (2010), Mustonen et al (2008, Coletti et al (2009), Ouyang (2005), Lima et al (2010) e González et al (2011) empregaram análise multivariada a dados de qualidade de água em diferentes bacias hidrográficas.…”

Section: Introductionunclassified

Qualidade da água superficial por meio de análise do componente principal

Finkler¹,

Peresin²,

Cocconi³

et al. 2015

Rev. ambiente água

View full text Add to dashboard Cite

This study used multivariate techniques for data analysis in order to determine the natural and anthropogenic factors that contribute to the spatial and temporal variations of water quality in urban watersheds of Caxias do Sul, Brazil. Principal Component Analysis (PCA) was used to analyze data collected at 30 points between September 2012 and January 2014. Monitoring was conducted bimonthly in six urban basins, where a total of 21 physical, chemical and biological parameters were analyzed. We found that PCA can explain 71.3% of the total variance in water quality, and that domestic and industrial pollution are the main Qualidade da água superficial por meio de análise … Rev. Ambient. Água vol. 10 n. 4 Taubaté-Oct. / Dec. 2015 contributors to the water quality variation in the region, especially from the galvanic manufacturing sector. Furthermore, we observed a trend of self-attenuation of pollutants in water downstream from urban areas and great anthropogenic influence as the pressure from urbanized areas decreases.

show abstract

Análises de Componente Principal e de Agrupamento para estudo de ventos para a geração de energia eólica na região do Ceará, Paraíba, Pernambuco e Rio Grande do Norte, Brasil

Cited by 8 publications

References 7 publications

No-tillage system participatory quality index

No-tillage system participatory quality index

Análise de Agrupamento na Otimização de Futuras Aplicações de Modelagem Numérica na Estimativa e Previsão de Recurso Eólico (Research for Optimization of Numerical Modeling Applied to Estimate and Forecast Wind Resource)

Qualidade da água superficial por meio de análise do componente principal

Contact Info

Product

Resources

About