Leandro Mineti

Treinamento de um único modelo de linguagem pode consumir o equivalente ao consumo de centenas de residências em um ano

Thoughts about integrating decision systems into business processes.

Como os dados poderiam nos ajudar a enfrentar pandemias.

Multi-step forecasting of wind speed time series, especially for day-ahead and longer time horizons, is still a challenging problem in the wind energy sector. In this paper, a novel analog-based methodology to perform multi-step forecasting in univariate time series, named dynamic time scan forecasting (DTSF), is presented. DTSF is a fast time series forecasting methodology for large data sets. Thus, the proposed method is optimal for forecasting renewable energy features such as wind speed, in… Mostrar más

Multi-step forecasting of wind speed time series, especially for day-ahead and longer time horizons, is still a challenging problem in the wind energy sector. In this paper, a novel analog-based methodology to perform multi-step forecasting in univariate time series, named dynamic time scan forecasting (DTSF), is presented. DTSF is a fast time series forecasting methodology for large data sets. Thus, the proposed method is optimal for forecasting renewable energy features such as wind speed, in which standard statistical and soft computing methods present limitations. A scan procedure is applied to identify similar patterns, named best matches, throughout the time series. As opposed to euclidean distance, more flexible similarity functions, using polynomial regression models, are dynamically estimated and Goodness-of-fit statistics are used to find the best matches. The observed values following the best matches and the fitted similarity functions are used to predict k-steps ahead, as well as forecasting intervals. An ensemble version of the method, named eDTSF, combines different predictions using different set of parameters thus, further improving forecasting performance. Remarkably, eDTSF achieved competitive results for multi-step forecasting of wind speed time series, even in situations of very high variability, as compared to eleven selected concurrent forecasting methods. Mostrar menos

Quatro pilares, em atuação interdependente, permitem antecipar os próximos anos das empresas: sustentabilidade, capital, trabalho e inovação.

A concise description of the main concepts around Artificial Intelligence and Machine Learning.

An original application of an approach first used in epidemiology investigation was developed and implemented in energy regulation benchmarking. Using Brazilian electricity energy distribution utilities, the proposed methodology applies spatial Bayesian analysis to estimate the number of clusters and the utilities in each cluster. By dividing the utilities into smaller, but geographically closer groups, it can be argued that local determinants of production or environmental components are… Mostrar más

An original application of an approach first used in epidemiology investigation was developed and implemented in energy regulation benchmarking. Using Brazilian electricity energy distribution utilities, the proposed methodology applies spatial Bayesian analysis to estimate the number of clusters and the utilities in each cluster. By dividing the utilities into smaller, but geographically closer groups, it can be argued that local determinants of production or environmental components are ed in the benchmarking model. Thus, the proposed method requires the spatial location of the utilities and their cost efficiencies, estimated previously by the regulator. Results show two detected clusters with high and low efficiencies located in the east and west of Brazil. After applying the regulator model to the detected groups, significant changes in cost efficiencies were estimated for a few utilities. This is important information that can be used by the regulator to estimate future cost incentives. Mostrar menos

An torch ignition system with homogeneous charge is numerically analyzed using a one-dimensional computational model. The new ignition system is implemented in a four-cylinder engine, spark ignition, 1600 cm3, 16 valves. Parameters such as mass burn fraction profile and pressure vs crank angle are compared with experimental data obtained with the torch ignition system operating homogeneous charge with stoichiometric mixture. The computational model uses information such as the pre-chamber… Mostrar más

An torch ignition system with homogeneous charge is numerically analyzed using a one-dimensional computational model. The new ignition system is implemented in a four-cylinder engine, spark ignition, 1600 cm3, 16 valves. Parameters such as mass burn fraction profile and pressure vs crank angle are compared with experimental data obtained with the torch ignition system operating homogeneous charge with stoichiometric mixture. The computational model uses information such as the pre-chamber pressure as a function of crack angle, intake and exhaust pressure, volumetric efficiency, maps of injection and ignition, valve discharge and valve intake coefficient, lifting valve, laminar flame speed, among others parameters. A simulation model of the torch ignition system, after being validated with experimental data, should provide information on increasing the burning rate, possibility of burning lean mixtures, more complete burn of the mixture, less sensitivity to composition and state of the mixture among others points that could be improved in control and ignition system design by torch ignition. To simulate the GT-POWER program and the results will used to obtain important information of the air flow and combustion in the cylinder in order to improve the original design of the torch ignition system. Mostrar menos