A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

Non-invasive methodology for the identification of plastic pieces in museum environment – a novel approach

The preservation of modern and contemporary art and costume collections in museums requires a complete understanding of their constituent materials which are often synthetic or semi-synthetic polymers. An extraordinary amount of quality information can be gained from instrumental techniques, but some of them have the disadvantage of being destructive. This paper presents a new totally integrated non-invasive methodology, for the identification of polymers and their additives, on plastic artefacts in museums. NMR (nuclear magnetic resonance) and in-situ FTIR-ATR (attenuated total reflection infrared spectroscopy) combination allowed the full characterization of the structure of thesematerials and correct identification of each one. The NMR technique applied to leached surface exudates identified unequivocally a great number of additives, exceeding the Py–GC–MS analysis of micro-fragments in number and efficiency. Additionally, in-situ FTIR-ATR provided exactly the same information of the destructive μ-FTIR about the polymer structure and confirmed the presence of some additives. Eight costume pieces (cosmetic boxes and purses), dating to the beginning of the 20th century and belonging to the Portuguese National Museum of Costume and Fashion, were correctly identified with this new integrated methodology, as beingmade of plastics derived fromcellulose acetate or cellulose nitrate polymers, contradicting the initial information that these pieces were made of Bakelite. The identification of a surprisingly large number of different additives forms an added value of this methodology and opens a perspective of a quick and better characterization of plastic artefacts in museum environments.

An Approach to Tactical Performance Optimization in a Big Data World

The amount of data collected from an individual player during a football match has increased significantly in recent years, following technological evolution in positional tracking. However, given the short time that separates competitions, the common analysis of these data focuses on the magnitude of actions of each player, while considering either technical or physical perform- ance. This focus leads to a considerable amount of information not being taken into account in performance optimization, particularly while considering a sequence of different matches of the same team. In this presentation, we will present a tactical performance indicator that considers players’ overall positioning and their level of coordination during the match. This performance indicator will be applied in different time scales, with a particular focus on possible practical applications.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.