Optimal Control Strategies for Switchable Roof Insulation Systems Applied to US Residential Buildings

[1]  Carlos Silva,et al.  Dynamic programming and genetic algorithms to control an HVAC system: Maximizing thermal comfort and minimizing cost with PV production and storage , 2017 .

[2]  Moncef Krarti,et al.  Energy performance analysis of variable thermal resistance envelopes in residential buildings , 2015 .

[3]  Hashem Akbari,et al.  Effect of cool roofs on commercial buildings energy use in cold climates , 2016 .

[4]  Beliz Ozorhon,et al.  A simulation-based optimization method for designing energy efficient buildings , 2018, Energy and Buildings.

[5]  Jin-Lee Kim.  Examining the relationship between algorithm stopping criteria and performance using elitist genetic algorithm , 2010, Proceedings of the 2010 Winter Simulation Conference.

[7]  D. Sailor,et al.  Potential energy and climate benefits of super-cool materials as a rooftop strategy , 2019, Urban Climate.

[8]  S. Algarni.  Potential for cooling load reduction in residential buildings using cool roofs in the harsh climate of Saudi Arabia , 2018, Energy & Environment.

[9]  Bijan Adl-Zarrabi,et al.  Effect from a Variable U-Value in Adaptive Building Components with Controlled Internal Air Pressure , 2015 .

[10]  S. A. Al-Sanea,et al.  Effect of thermal mass on performance of insulated building walls and the concept of energy savings potential , 2012 .

[11]  H. K. Rangavittal,et al.  Genetic Algorithm Parameter Effect on 3D Truss Optimization with Discrete Variable , 2018, Advanced Journal of Graduate Research.

[12]  Marco Perino,et al.  Switching from static to adaptable and dynamic building envelopes: A paradigm shift for the energy efficiency in buildings , 2015 .

[13]  Moncef Krarti,et al.  A review of benefits and limitations of static and switchable cool roof systems , 2017 .

[14]  Antonio Bolufé Röhler,et al.  Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution , 2014, Applied Intelligence.

[15]  Alberto Martilli,et al.  A New Modeling Approach to Forecast Building Energy Demands During Extreme Heat Events in Complex Cities , 2013 .

[16]  Jlm Jan Hensen,et al.  Framework for assessing the performance potential of seasonally adaptable facades using multi-objective optimization , 2014 .

[17]  G. P. Peterson,et al.  Investigation of a Novel Flat Heat Pipe , 2005 .

[18]  William W. Clark,et al.  Configuring wall layers for improved insulation performance , 2013 .

[19]  Ryan Sharston,et al.  The combined effects of thermal mass and insulation on energy performance in concrete office buildings , 2020, Advances in Building Energy Research.

[20]  Jan Hensen,et al.  Investigating the potential of a closed-loop dynamic insulation system for opaque building elements , 2018, Energy and Buildings.

[21]  William W. Clark,et al.  Conceptual analysis and design of a partitioned multifunctional smart insulation , 2014 .

[22]  Moncef Krarti,et al.  Control strategies for dynamic insulation materials applied to commercial buildings , 2017 .

[23]  D. Sailor,et al.  Effects of Rooftop Photovoltaics on Building Cooling Demand and Sensible Heat Flux Into the Environment for an Installation on a White Roof , 2020 .

[24]  Valentina Serra,et al.  Dynamic insulation systems: experimental analysis on a parietodynamic wall , 2015 .

[25]  Moncef Krarti,et al.  Evaluation of energy savings potential of variable reflective roofing systems for US buildings , 2017 .

[26]  Fabio Favoino,et al.  Design and control optimisation of adaptive insulation systems for office buildings. Part 2: A parametric study for a temperate climate , 2017 .

[27]  T. Song,et al.  Pillar-supported vacuum insulation panel with multi-layered filler material , 2016 .

[28]  Masayuki Mae,et al.  Anti-insulation mitigation by altering the envelope layers’ configuration , 2017 .

[29]  Francesco Fiorito,et al.  Exploration of Adaptive Origami Shading Concepts through Integrated Dynamic Simulations , 2018, Journal of Architectural Engineering.

[30]  Ryozo Ooka,et al.  Optimal design method for building energy systems using genetic algorithms , 2009 .

[31]  Yacine Rezgui,et al.  A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control , 2018 .

[32]  Bjørn Petter Jelle,et al.  Comparison of the energy saving potential of adaptive and controllable smart windows: A state-of-the-art review and simulation studies of thermochromic, photochromic and electrochromic technologies , 2019, Solar Energy Materials and Solar Cells.

[33]  X. Yu,et al.  Adaptive thermochromic roof system: Assessment of performance under different climates , 2019, Energy and Buildings.

[34]  M. O. Odetayo.  Optimal population size for genetic algorithms: an investigation , 1993 .

[35]  Amir H. Ghoreishi,et al.  Parametric study of thermal mass property of concrete buildings in US climate zones , 2013 .

[36]  Ammar H. A. Dehwah,et al.  Impact of switchable roof insulation on energy performance of US residential buildings , 2020, Building and Environment.

[37]  Theocharis Tsoutsos,et al.  On the selection and design of the proper roof pond variant for passive cooling purposes , 2011 .

[38]  Niccolò Aste,et al.  The influence of the external walls thermal inertia on the energy performance of well insulated buildings , 2009 .

[39]  Olympia Roeva,et al.  Influence of the population size on the genetic algorithm performance in case of cultivation process modelling , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[40]  Z. El Maskaoui.  Genetic Algorithm Parameters Effect on the Optimal Structural Design Search , 2017 .

[41]  C. Heaviside,et al.  Potential benefits of cool roofs in reducing heat-related mortality during heatwaves in a European city. , 2019, Environment international.

[42]  Bianca R. Capra,et al.  The potential for cool roofs to improve the energy efficiency of single storey warehouse-type retail buildings in Australia: A simulation case study , 2017 .

[43]  Moncef Krarti,et al.  Optimization of envelope and HVAC systems selection for residential buildings , 2011 .

[44]  Di Wang,et al.  Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design , 2015 .

[45]  M. Krarti,et al.  Analysis of multi-step control strategies for dynamic insulation systems , 2019 .

[46]  Fabio Favoino,et al.  Design and control optimisation of adaptive insulation systems for office buildings. Part 1: Adaptive technologies and simulation framework , 2017 .

[47]  Luisa F. Cabeza,et al.  Cool Roof Impact on Building Energy Need: The Role of Thermal Insulation with Varying Climate Conditions , 2019, Energies.

[48]  Hosny Z. Abou-Ziyan,et al.  Achieving annual and monthly net-zero energy of existing building in hot climate , 2016 .

[49]  Christoph Maurer,et al.  Modeling of facade elements with switchable U-value , 2018 .

[50]  Aidan Reilly,et al.  The impact of thermal mass on building energy consumption , 2017 .

[51]  Jlm Jan Hensen,et al.  EXPLORING THE POTENTIAL OF CLIMATE ADAPTIVE BUILDING SHELLS , 2011 .

[52]  Mahmood Alam,et al.  Energy and economic analysis of Vacuum Insulation Panels (VIPs) used in non-domestic buildings , 2017 .