A Novel Method Based on Neural Networks for Designing Internal Coverings in Buildings: Energy Saving and Thermal Comfort

Although several papers define energy saving and thermal comfort optimization with internal coverings materials, none of them deal with predictive models to improve design in building constructions. Thus, artificial intelligence (AI) procedures were applied in this paper. In particular, neural networks (NNs) were designed for indoor ambiences with internal covering materials in different buildings, were trained and employed to predict indoor ambiences (indoor temperature and relative humidity as a function of weather conditions), and, based on these procedures, local thermal comfort conditions and energy consumption, due to the type of internal covering permeability level, were calculated. Results from this original methodology showed a better acceptability of indoor ambiences when permeable coating materials were used, in agreement with previous research works. At the same time, with permeable coverings, a lower energy consumption of 20% in the heating, ventilation, and air conditioning (HVAC) systems was needed to reach more comfortable conditions during the summer season in the first hours of occupation. Finally, all these results suggest an original methodology to optimize indoor ambiences based on the design of internal coverings by NN.

[1]  Bojana Stanković,et al.  Building stock characteristics and energy performance of residential buildings in Eastern-European countries , 2016 .

[2]  Anton Trník,et al.  Modeling of heat capacity peaks and enthalpy jumps of phase-change materials used for thermal energy storage , 2017 .

[3]  Hicham Johra,et al.  Influence of internal thermal mass on the indoor thermal dynamics and integration of phase change materials in furniture for building energy storage: A review , 2017 .

[4]  José A. Orosa,et al.  Eco-friendly Pressure Drop Dehumidifier: An Experimental and Numerical Analysis , 2019, Sustainability.

[5]  Mustafa Oral,et al.  Pattern Layer Reduction for a Generalized Regression Neural Network by Using a Self–Organizing Map , 2018, Int. J. Appl. Math. Comput. Sci..

[6]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[7]  José A. Orosa,et al.  Improving PAQ and comfort conditions in Spanish office buildings with passive climate control , 2009 .

[8]  P. Fanger,et al.  Upper limits for indoor air humidity to avoid uncomfortably humid skin , 1998 .

[9]  Tim Padfield.  The role of absorbent building materials in moderating changes of relative humidity: Ph.D.thesis , 1999 .

[10]  Samuel V. Glass,et al.  Moisture Performance of Energy-Efficient and Conventional Wood-Frame Wall Assemblies in a Mixed-Humid Climate , 2015 .

[11]  J. A. Orosa,et al.  Passive climate control in Spanish office buildings for long periods of time , 2008 .

[12]  C. Simonson,et al.  The effect of structures on indoor humidity--possibility to improve comfort and perceived air quality. , 2002, Indoor air.

[13]  P. Fanger,et al.  Upper limits of air humidity for preventing warm respiratory discomfort , 1998 .

[14]  Tadeusz Tatara,et al.  Thermal and Vibration Comfort Analysis of a Nearly Zero-Energy Building in Poland , 2018, Sustainability.

[15]  José A. Orosa,et al.  Energy saving with passive climate control methods in Spanish office buildings , 2009 .

[16]  Monjur Mourshed,et al.  Model-Based Optimal Control of Window Openings for Thermal Comfort , 2018, Proceedings.

[17]  Nuno M.M. Ramos,et al.  EXPERIMENTAL QUANTIFICATION OF THE OPERATIVE TIME OF A PASSIVE HVAC SYSTEM USING POROUS COVERING MATERIALS , 2010 .

[18]  José A. Orosa,et al.  Reducing energy peak consumption with passive climate control methods , 2011 .

[19]  Hussein J. Akeiber,et al.  An overview of phase change materials for construction architecture thermal management in hot and dry climate region , 2017 .

[20]  Stéphane Hameury,et al.  Contribution of indoor exposed massive wood to a good indoor climate: in situ measurement campaign , 2004 .

[21]  Prabal Talukdar,et al.  An experimental data set for benchmarking 1-D, transient heat and moisture transfer models of hygroscopic building materials. Part II: Experimental, numerical and analytical data , 2007 .

[22]  María del Mar Castilla,et al.  Repetitive Control to Improve Users’ Thermal Comfort and Energy Efficiency in Buildings , 2018 .

[23]  Ramasamy Alagirusamy,et al.  Effect of structural parameters on thermal protective performance and comfort characteristic of fabrics , 2017 .

[24]  Polat Hancer,et al.  A Model for External Walls Selection in Hot and Humid Climates , 2018, Sustainability.

[25]  Heather Cruickshank,et al.  Improving Thermal Comfort of Low-Income Housing in Thailand through Passive Design Strategies , 2017 .

[26]  José A. Orosa,et al.  A Novel Method for nZEB Internal Coverings Design Based on Neural Networks , 2019 .

[27]  Claudio M. García-Herrera,et al.  Thermal simulation of a social dwelling in Chile: Effect of the thermal zone and the temperature-dependant thermophysical properties of light envelope materials , 2017 .

[28]  Carey J. Simonson,et al.  Moisture buffering capacity of hygroscopic building materials: Experimental facilities and energy impact , 2006 .

[29]  P. Fanger,et al.  Impact of Temperature and Humidity on Perception of Indoor Air Quality During Immediate and Longer Whole‐Body Exposures , 1998 .

[30]  Asta Nicolajsen.  Thermal transmittance of a cellulose loose-fill insulation material , 2005 .