Indoor thermal comfort assessment using different constructive solutions incorporating PCM

António Figueiredo, Romeu Vicente, José Lapa, Claudino Cardoso, Fernanda Rodrigues and Jérôme Kämpf

Applied Energy, 2017, vol. 208, issue C, No S0306261917313156, 1208-1221

Abstract: Sustainable energy and thermal retrofit design of buildings or districts has a strong global impact in the viewpoint of economies and energy-efficiency perspectives. Several aspects such as architectonic design, building materials, construction technology, mechanical systems and outdoor climate determines the thermal behaviour of buildings and their ability to provide indoor thermal comfort to occupants. The use of geothermal energy and phase change materials (PCMs) in the construction systems are an opportunity that may attenuate indoor air temperature fluctuation as well as overheating risk. This paper presents the results of a study on indoor thermal comfort and energy efficiency regarding the PCM’s positive role when applied to new constructive solutions, inside a building with a geothermal system linked to the air conditioning system. The PCM study was based on real and simulated investigations in two rooms of a new university department at the Aveiro campus. Higrothermal monitoring (indoor air temperature) of two rooms in which one of them has PCM panels incorporated into gypsum board partition wall and into a suspended ceiling. The scope was driven to investigate the potential of these solutions for overheating mitigation. The numerical study was conducted by using an evolutionary algorithm coupled with the software EnergyPlus® used in simulations. In the scope of this optimization process, constructive solutions with the incorporation of different types of PCM with different melting temperatures and enthalpy, and different flow rates of natural ventilation were combined to investigate the potential and the payback time of these novel solutions.

Keywords: Phase change materials; Thermal comfort; Overheating; Dynamic building simulation; Optimization; Evolutionary algorithms (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2017.09.032

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