A statistical approach for the evaluation of the thermal behavior of dry assembled PCM containing walls |
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Authors: | Mario De Grassi Alessandro Carbonari Giulio Palomba |
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Affiliation: | 1. Department of Architecture, Construction and Structures, Engineering Faculty, Polytechnic University of Marche via delle Brecce Bianche, 60131 Ancona, Italy;2. Economics Department, Economics Faculty “Giorgio Fuá”, Polytechnic University of Marche, Piazzale Martelli 8, 60121 Ancona, Italy |
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Abstract: | Adequate estimation of the energetic improvements which derived from the insertion of phase change materials (PCM) inside light dry assembled walls is an important step in order to quantify the comfort advantages that can derive from the use of such materials. The use of a statistic approach based fundamentally on the time series analysis method may represent a valid information instrument in support of all the technical analysis carried out in order to evaluate the effects of thermal inertia increase on the heat transmission process that occurs between the elements of a building. PCM containing walls, tested at the “Renewable Energies Outdoor Laboratory” of the Polytechnic University of Marche during the summer of 2003, present delicate problems relative to the identification of physical models relating to dynamics of heat exchange between building components. The application of a vector auto regressive (VAR) estimation model allows, using high frequency experimental data, obtaining consistent estimates regarding the physical phenomenon of energy exchange which intervene inside buildings—both between the walls, and through the walls—occurring during the observation period. The results of this approach are twofold: firstly, they demonstrate the existence of statistically significant linear dependencies among the variables used, and secondly, they highlight the comfort conditions’ improvements due to the insertion of PCM inside dry assembled walls. |
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Keywords: | Phase change materials Heat transfer Time series analysis Stochastic processes VAR models |
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