Persona:
Sama Meige, Miguel Ángel

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Sama Meige
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Miguel Ángel
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2019 - 201912020 - 20221
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  • Miniatura
    Publicación
    Analysis of smart thermostat thermal models for residential building
    (Elsevier, 2022-06-02) Arias, J.; Khan, A.A.; Rodriguez Uría, J.; Sama Meige, Miguel Ángel
    This work studies the thermal behavior of residential buildings by using the data provided by smart thermostats and weather forecast data. We consider an equivalent circuit model depending on four parameters related to the heater power, the solar energy, heat capacity, and the thermal resistance of the building. We employ a random ordinary differential equation to overcome the natural model uncertainty. We develop a differential equation constrained least-squares-based parameter identification approach for uncertainty quantification. We provide results related to the analytic properties of the parameter-to-solution map involving a derivative characterization. Furthermore, using a discretization scheme adapted to the given data, we derive discrete formulas for the deterministic identification model and compute the statistical moments of the random model. Following a machine learning approach, we propose an algorithm that consists of three phases. The first is a training phase where we identify the parameter uncertainties on a training dataset. In the second phase, we establish a normal distribution of the parameters using these uncertainties. In the final phase, we simulate the temperature on a test dataset by solving the random model. We test this algorithm on real data from two residential buildings. The detailed numerical experiments show the feasibility and the efficacy of the developed framework.
  • Miniatura
    Publicación
    Limit Behavior of Approximate Proper Solutions in Vector Optimization
    (Society for Industrial and Applied Mathematics, 2019) Gutiérrez, C.; Novo, V.; Huerga Pastor, Lidia; Sama Meige, Miguel Ángel
    In the framework of a vector optimization problem, we provide conditions for approximate proper solutions to tend to exact weak/efficient/proper solutions when the error tends to zero. This limit behavior depends on an approximation set that is used to define the approximate proper efficient solutions. We also study the special case when the final space of the vector optimization problem is normed, and more particularly, when it is finite dimensional. In these specific frameworks, we provide several explicit constructions of dilating ordering cones and approximation sets that lead to the desired limit behavior. In proving our results, new relationships between different concepts of approximate proper efficiency are stated.