Dentre algumas fontes renováveis de energia, destacam-se os ventos. No Brasil, em especial na região Nordeste, tem-se observado avanços quanto aos estudos e investimentos em localidades consideradas potenciais produtoras de energia eólica. Neste contexto, podem ser empregadas as funções densidade de probabilidade de modelos de distribuição como forma auxiliar à tomada de decisão sobre a escolha de uma determinada região para a instalação de parques eólicos. Neste trabalho, buscou-se analisar o potencial eólico para geração de energia proveniente dos ventos em Petrolina-PE, com a série histórica de velocidade do vento de 01/01/2015 a 31/12/2016 e através da comparação entre os ajustes realizados pelas distribuições Weibull com dois parâmetros (Weibull-2p) e Burr, bem como a partir da análise da velocidade média do vento verificada na região. Além disso, observar a direção predominante dos ventos por meio da Rosa dos Ventos. Para a estimação dos parâmetros das distribuições, foi adotado o Método da Máxima Verossimilhança (MMV) que tem alcançado valores ótimos em relação a outros métodos de estimativa de parâmetros. Os critérios de seleção AIC e BIC, a estatística de Anderson-Darling e as acurácias MAPE e MAD foram adotadas para a avaliação da bondade dos ajustes das distribuições, onde se verificou que a Weibull-2p forneceu melhor modelagem aos dados analisados. A direção predominante dos ventos encontrada foi a sudeste, com variação entre ~105º e ~135º e velocidade média de 8,4m/s. Com os resultados obtidos, a região estudada alcançou, segundo classificação do NREL, avaliação esplêndida para a viabilidade de geração de energia eólica.

This study evaluates the Brazilian agricultural commodities market and the dollar–real exchange price variation using the multifractal detrended fluctuations analysis methodology. We investigated the period from January 1, 2019 to September 25, 2019, outside the COVID-19 pandemic, and from January 1, 2020 to September 25, 2020, during the COVID-19 pandemic. We verified the fluctuations of commodities and dollar–real exchange prices during the pandemic caused by COVID-19 showed a record price. The results of Hurst exponent and multifractal parameters [Formula: see text], [Formula: see text], and [Formula: see text] indicate that during the COVID-19 pandemic, sugar was the most efficient commodity, while pork the less one. Compared to the identical months in 2019, the dollar–real exchange was the most efficient market, while ethanol was the least efficient.

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Financial markets are complex systems in which traders interact using the most varied strategies. Computational techniques that use intelligent agents can assist in decision making in order to maximize gains. In this sense, the objective of this article is to observe the behavior of financial agents participating in simulated markets and infer about the gains of these agents. Through the Particle Swarm Optimization algorithm, we used two distinct groups of intelligent agents: one group uses a degree of belief in the prediction of assets for the next day and the other group does not use, in which both interact with each other seeking to maximize their gains. An exploratory research was carried out, with quantitative analysis on the data. The results showed that the group that uses the forecast is more homogeneous, showing higher average wealth gains, with capital and acquired stock concentrations varying according to the historical price series used (Bitcoin, Ethereum, Litcoin, or Ripple). Therefore, the implemented procedure can be improved and used for the development of environments aimed at a better understanding of financial markets and assisting market participants in the definition of trading strategies that enable the minimization of financial losses.

With the growing popularity of digital currencies known as cryptocurrencies, there is a need to develop models capable of robustly analyzing and predicting the value of future returns in these markets. In this article, we extract behavior rules to predict the values of future returns in the Bitcoin, Ethereum, Litecoin, and Ripple closing series. We used categorical data in the analyses and Markov chain models from the first to the tenth order to propose a new way of establishing possible future scenarios, in which we analyze the dependence of memory on the dynamics of the process. We used the measurements of accuracy Mean Quadratic Error, Absolute Error Mean Percentage, and Absolute Standard Deviation for the choice of the best models. Our findings reveal that cryptocurrencies have long-range memory. Bitcoin, Ethereum, and Ripple exposed seven steps of memory, while Litecoin displayed nine memory steps. From the transitions between states that happened the most, we defined decision rules that assisted in the definition of future returns in the series. Our results can support the decisions of traders, investors, crypto-traders, and policy-makers.