Predicting Global Irradiance Combining Forecasting Models Through Machine Learning

This paper has been presented at : 13th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2018)Predicting solar irradiance is an active research problem, with many physical models having being designed to accurately predict Global Horizontal Irradiance. However, some of the models are better at short time horizons, while others are more accurate for medium and long horizons. The aim of this research is to automatically combine the predictions of four different models (Smart Persistence, Satellite, Cloud Index Advection and Diffusion, and Solar Weather Research and Forecasting) by means of a state-of-the-art machine learning method (Extreme Gradient Boosting). With this purpose, the four models are used as inputs to the machine learning model, so that the output is an improved Global Irradiance forecast. A 2-year dataset of predictions and measures at one radiometric station in Seville has been gathered to validate the method proposed. Three approaches are studied: a general model, a model for each horizon, and models for groups of horizons. Experimental results show that the machine learning combination of predictors is, on average, more accurate than the predictors themselves.The authors are supported by the Spanish Ministry of Economy and Competitiveness, projects ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R and FEDER funds. Some of the authors are also funded by the Junta de Andalucía (research group TEP-220)

Genetic Analysis of Hematological Parameters in Incipient Lines of the Collaborative Cross

Hematological parameters, including red and white blood cell counts and hemoglobin concentration, are widely used clinical indicators of health and disease. These traits are tightly regulated in healthy individuals and are under genetic control. Mutations in key genes that affect hematological parameters have important phenotypic consequences, including multiple variants that affect susceptibility to malarial disease. However, most variation in hematological traits is continuous and is presumably influenced by multiple loci and variants with small phenotypic effects. We used a newly developed mouse resource population, the Collaborative Cross (CC), to identify genetic determinants of hematological parameters. We surveyed the eight founder strains of the CC and performed a mapping study using 131 incipient lines of the CC. Genome scans identified quantitative trait loci for several hematological parameters, including mean red cell volume (Chr 7 and Chr 14), white blood cell count (Chr 18), percent neutrophils/lymphocytes (Chr 11), and monocyte number (Chr 1). We used evolutionary principles and unique bioinformatics resources to reduce the size of candidate intervals and to view functional variation in the context of phylogeny. Many quantitative trait loci regions could be narrowed sufficiently to identify a small number of promising candidate genes. This approach not only expands our knowledge about hematological traits but also demonstrates the unique ability of the CC to elucidate the genetic architecture of complex traits

Single and blended models for day-ahead photovoltaic power forecasting

Solar power forecasts are gaining continuous importance as the penetration of solar energy into the grid rises. The natural variability of the solar resource, joined to the difficulties of cloud movement modeling, endow solar power forecasts with a certain level of uncertainty. Important efforts have been carried out in the field to reduce as much as possible the errors. Various approaches have been followed, being the predominant nowadays the use of statistical techniques to model production. In this study, we have performed a comparison study between two extensively used statistical techniques, support vector regression (SVR) machines and random forests, and two other techniques that have been scarcely applied to solar forecasting, deep neural networks and extreme gradient boosting machines. Best results were obtained with the SVR technique, showing a nRMSE of 22.49%. To complete the assessment, a weighted blended model consisting on an average weighted combination of individual predictions was created. This blended model outperformed all the models studied, with a nRMSE of 22.24%. © Springer International Publishing AG 2017

Contribuições para uma agenda de desenvolvimento do Brasil

Inclui bibliografia e notas de rodapé. Apresentação -- A reorientação do desenvolvimento industrial -- A estrutura recente de proteção nominal e efetiva no Brasil / [coordenado pela economista Marta Reis Castilho, colaboradores Ana Urraca Ruiz ... [et al.] -- Uma nova agenda para a política de comércio exterior do Brasil / [preparado por Vera Thorstensen e Lucas Ferraz, colaboradores Carolina Muller e Thiago Nogueira] -- O cúmulo da cumulatividade tributária -- O comércio bilateral Brasil-China e a produção industrial brasileira