BUSCADOR RECOLECTA

Wind has been the largest contributor to the growth of renewal energy during the early 21st century. However, the natural uncertainty that arises in assessing the wind resource implies the occurrence of wind power forecasting errors which perform a considerable role in the impacts and costs in the wind energy integration and its commercialization. The main goal of this paper is to provide a deeper insight in the analysis of timing errors which leads to the proposal of a new methodology for its control and measure. A new methodology, based on Dynamic TimeWarping, is proposed to be considered in the estimation of accuracy as attribute of forecast quality. A new dissimilarity measure, the Temporal Distortion Index, among time series is introduced to complement the traditional verication measures found in the literature. Furthermore we provide a bi-criteria perspective to the problem of comparing different forecasts. The methodology is illustrated with several examples including a real case., This paper has been supported under Grants MTM 2012-36025 and DPI 490 2013-47279-C2-1-R. The authors are grateful to the research staff of the National Renewable Energy Center of Spain (CENER) for their help in the development of this new methodology of analysis of errors contributing with their prediction model LocalPred.

Recent years have seen a growing trend in wind and solar energy generation globally and it is expected that an important percentage of total energy production comes from these energy sources. However, they present inherent variability that implies uctuations in energy generation that are dicult to forecast. Thus, forecasting errors have a considerable role in the impacts and costs of renewable energy integration, management, and commercialization. This study presents an important advance in the task of analyzing prediction models, in particular, in the timing component of prediction error, which improves previous pioneering results. A new method to match time series is dened in order to assess energy forecasting accuracy. This method relies on a new family of step patterns, an essential component of the algorithm to evaluate the temporal distortion index (TDI). This family minimizes the mean absolute error (MAE) of the transformation with respect to the reference series (the real energy series) and also allows detailed control of the temporal distortion entailed in the prediction series. The simultaneous consideration of temporal and absolute errors allows the use of Pareto frontiers as characteristic error curves. Real examples of wind energy forecasts are used to illustrate the results., This paper has been supported under Grants MTM 2012-36025 and DPI 2013-47279-C2-1-R. The authors are grateful to the research staff of the National Renewable Energy Center of Spain (CENER) for their help in the development of this new methodology of error analysis through the contribution of their prediction model LocalPred.

[EN] The new concept archetypoids is introduced. Archetypoid analysis represents each observation in a dataset as a mixture of actual observations in the dataset, which are pure type or archetypoids. Unlike archetype analysis, archetypoids are real observations, not a mixture of observations. This is relevant when existing archetypal observations are needed, rather than fictitious ones. An algorithm is proposed to find them and some of their theoretical properties are introduced. It is also shown how they can be obtained when only dissimilarities between observations are known (features are unavailable). Archetypoid analysis is illustrated in two design problems and several examples, comparing them with the archetypes, the nearest observations to them and other unsupervised methods., The authors would like to thank Juan Domingo from the University of Valencia for providing the binary images of women’s
trunks. They would also like to thank the Biomechanics Institute of Valencia for providing them with the dataset and
the Spanish Ministry of Health and Consumer Affairs for having promoted and coordinated the ‘‘Anthropometric Study of the Female Population in Spain’’. The authors are also grateful to the Associate Editor and two reviewers for their very
constructive suggestions, which have led to improvements in the manuscript. This work has been partially supported by
Grant DPI2013-47279-C2-1-R.

The internal lubricant content (ILC) of inhalation grade HPMC capsules is a key factor to ensure good powder release when the patient inhales a medicine from a dry powder inhaler (DPI). Powder release from capsules has been shown to be influenced by the ILC. The characteristics used to measure this are the emitted dose, fine particle fraction and mass median aerodynamic diameter. In addition the ILC level is critical for capsule shell manufacture because it is an essential part of the process that cannot work without it. An experiment has been applied to the manufacture of inhalation capsules with the required ILC. A full factorial model was used to identify the controlling factors and from this a linear model has been proposed to improve control of the process. (C) 2016 Elsevier B.V. All rights reserved., This research was supported by Qualicaps Europe, S.A.U.; Spanish DGI Grant no. MTM2014-58159-P (M.T. Gasso) and G. Ayala (DPI2013-47279-C2-1-R),

Size fitting is a significant problem for online garment shops. The return rates due to size misfit are very high. We propose an ensemble (with an original and novel definition of the weights) of ordered logistic regression and random forest (RF) for solving the size matching problem, where ordinal data should be classified. These two classifiers are good candidates for combined use due to their complementary characteristics. A multivariate response (an ordered factor and a numeric value assessing the fit) was considered with a conditional random forest. A fit assessment study was carried out with 113 children. They were measured using a 3D body scanner to obtain their anthropometric measurements. Children tested different garments of different sizes, and their fit was assessed by an expert. Promising results have been achieved with our methodology. Two new measures have been introduced based on RF with multivariate responses to gain a better understanding of the data. One of them is an intervention in prediction measure defined locally and globally. It is shown that it is a good alternative to variable importance measures and it can be used for new observations and with multivariate responses. The other proposed tool informs us about the typicality of a case and allows us to determine archetypical observations in each class. (C) 2016 Elsevier Ltd. All rights reserved., This work has been partially supported by Grants DPI2013-47279-C2-1-R and DPI2013-47279-C2-2-R.