Ortíz Rodríguez, Samira Esther

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    Energy data analytics in smart rural communities
    (2020-10-29) Ortíz Rodríguez, Samira Esther; Castro Sitiriche, Marcel; College of Engineering; Ndoye, Mandoye; Rodríguez, Domingo; Andrade, Fabio; Department of Electrical and Computer Engineering; Ramírez Durand, Lillian
    The route to achieve the broad deployment of smart grids is becoming more and more complex because of the large amount of sensors, such as smart meters that are being included in future power systems. Along with the widespread use of smart meters, a large amount of valuable electricity data is collected, which needs to be processed and translated in its appropriate social context for end-users and utilities. Methodologies on data analytics that are emerging provide many ways to integrate social complexities that will provide value when applied on power data. Data analysis methodologies are applied for a smart interpretation and prescription that is relevant to the social benefit of the consumers. This work also proposes multidimensional parallel including social and technical aspects. In this study, power consumption is monitored to analyze the impact of energy usage on people’s well-being. There has been always a matter to keep the energy consumption at minimum or keep it among a range. The problem is now even more complex by adding a customer ’s satisfaction variable so we can ensure with the system configuration that customers can achieve the quality of life that could be derived from how energy is used by the different devices. The concept of the shapley value is used from cooperative game theory to solve the multi-objective optimization problem (MOO) to responsibly fulfill user’s satisfaction by maximizing satisfaction while minimizing the power consumption, with energy constrains since highly limited resources scenarios are studied. The novel method introduces the concept of a quantifiable user satisfaction, which is used to propose the concepts of power of satisfaction (PS) and energy of satisfaction (ES). Scenarios with energy constraints are considered. The proposed technique determine in which hours, the energy should be allocated to maximize the ES for each scenario, and then it is compared to the case in which devices are usually operated. Through the proposed technique, the satisfaction was superior in each scenario.
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    Publication
    Algoritmos de Dithering con tiempo de procesamiento reducido
    (2016) Ortíz Rodríguez, Samira Esther; Hunt, Shawn D.; College of Engineering; Rodríguez, Domingo; Santana Morant, Dámaris; Department of Electrical and Computer Engineering; Ríos, Isabel
    En esta investigación se trabajó con el algoritmo Pendiente Máxima (SD o Steepest Descent, por sus siglas en inglés) para hallar una señal de dither (señal aleatoria) que hace que el ruido de cuantización (diferencia entre la señal de entrada y de salida del cuantizador) sea blanco y no esté correlacionado con la señal original. El tiempo de procesamiento resultó ser menor al registrado por la investigación de punta. El algoritmo propuesto resultó ser menos complejo, empleó 99.2% menos multiplicaciones y 17.9% menos sumas que el algoritmo de Benitez-Quiroz [2]. El algoritmo de Pendiente Máxima tuvo un tiempo de procesamiento un 83.02% menor que el Benitez-Quiroz. Se usa además la autocorrelación circular del error de cuantización para verificar que sea un ruido blanco. Se obtienen valores de SNR mayor al que se obtiene con el dither triangular.