Examinando por Autor "Colmenar, Jose Manuel"

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A Practical Methodology for Reproducible Experimentation: An Application to the Double-Row Facility Layout Problem
(MIT Press Direct, 2023) Martin-Santamaria, Raul; Cavero, Sergio; Herran, Alberto; Duarte, Abraham; Colmenar, Jose Manuel
Reproducibility of experiments is a complex task in stochastic methods such as evolu- tionary algorithms or metaheuristics in general. Many works from the literature give general guidelines to favor reproducibility. However, none of them provide both a practical set of steps and also software tools to help on this process. In this paper, we propose a practical methodology to favor reproducibility in optimization prob- lems tackled with stochastic methods. This methodology is divided into three main steps, where the researcher is assisted by software tools which implement state-of-the- art techniques related to this process. The methodology has been applied to study the Double Row Facility Layout Problem, where we propose a new algorithm able to obtain better results than the state-of-the-art methods. To this aim, we have also repli- cated the previous methods in order to complete the study with a new set of larger instances. All the produced artifacts related to the methodology and the study of the target problem are available in Zenodo.
Accurate thermal prediction model for building-integrated photovoltaics systems using guided artificial intelligence algorithms
(ELSEVIER, 2022-06-01) Serrano-Lujan, Lucía; Toledo, Carlos; Colmenar, Jose Manuel; Abad, Jose; Urbina, Antonio
Progress in development of building-integrated photovoltaic systems is still hindered by the complexity of the physics and materials properties of the photovoltaic (PV) modules and its effect on the thermal behavior of the building. This affects not only the energy generation, as its active function and linked to economic feasibility, but also the thermal insulation of the building as part of the structure’s skin. Traditional modeling methods currently presents limitations, including the fact that they do not account for material thermal inertia and that the proposed semi-empirical coefficients do not define all types of technologies, mounting configuration, or climatic conditions. This article presents an artificial intelligence-based approach for predicting the temperature of a poly-crystalline silicon PV module based on local outdoor weather conditions (ambient temperature, solar irradiation, relative outdoor humidity and wind speed) and indoor comfort parameters (indoor temperature and indoor relative humidity) as inputs. A combination of two algorithms (Grammatical Evolution and Differential Evolution) guides to the creation of a customized expression based on the Sandia model. Different data-sets for a fully integrated PV system were tested to demonstrate its performance on three different types of days: sunny, cloudy and diffuse, showing relative errors of less than 4% in all cases and including night time. In comparison to Sandia model, this method reduces the error by up to 11% in conditions of variability of sky over short time intervals (cloudy days).