Multi-objective optimisation of building form, envelope and cooling system for improved building energy performance

Abstract This research integrates cooling systems as a variable in the multi-objective optimisation of building form and envelope design in the early design stages. The integration is achieved by 1) using a simplified calculation that requires minimal form, envelope and systems data for calculating the energy consumption of the cooling systems, and 2) optimising the cooling energy consumption by selecting the most efficient system automatically. The benefit of the integration is demonstrated in a case study using daylight as a second, potentially conflicting objective. The optimisation result is manually clustered and compared. The comparison reveals that the use of an efficient cooling system has the potential to achieve better trade-offs between the two conflicting objectives. These trade-offs are valuable information for designing the building form and envelope in the early design stages.

[1]  Risto Kosonen,et al.  A feasibility study of a ventilated beam system in the hot and humid climate: a case-study approach , 2005 .

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

[3]  Shady Attia,et al.  Simulation-based decision support tool for early stages of zero-energy building design , 2012 .

[4]  A. Rasheed,et al.  CITYSIM: Comprehensive Micro-Simulation of Resource Flows for Sustainable Urban Planning , 2009 .

[5]  Jianlei Niu,et al.  Energy savings potential of chilled-ceiling combined with desiccant cooling in hot and humid climates , 2002 .

[6]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[7]  Salvatore Carlucci,et al.  Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design , 2013 .

[8]  Stanley A. Mumma,et al.  Ceiling Radiant Cooling Panels as a Viable Distributed Parallel Sensible Cooling Technology Integrated with Dedicated Outdoor Air Systems , 2001 .

[9]  Farshad Kowsary,et al.  A novel approach for the simulation-based optimization of the buildings energy consumption using NSGA-II: Case study in Iran , 2016 .

[10]  Stanley A. Mumma,et al.  Chilled Ceilings in Parallel with Dedicated Outdoor Air Systems: Addressing the Concerns of Condensation, Capacity, and Cost , 2002 .

[11]  Toke Rammer Nielsen,et al.  Building energy optimization in the early design stages: A simplified method , 2015 .

[12]  Enrico Fabrizio,et al.  Energy systems in cost-optimized design of nearly zero-energy buildings , 2016 .

[13]  J. R. Correia,et al.  A general indirect representation for optimization of generative design systems by genetic algorithms: Application to a shape grammar-based design system , 2013 .

[14]  Ralph Evins,et al.  A review of computational optimisation methods applied to sustainable building design , 2013 .

[15]  Forrest Meggers,et al.  Assessment of the guidelines for zero-emission architectural design , 2010 .

[16]  Reinhard Radermacher,et al.  Theoretical study on separate sensible and latent cooling air-conditioning system , 2010 .

[17]  Luisa Caldas Evolving Three-Dimensional Architecture Form , 2002, AID.

[18]  Vítor Leal,et al.  Building envelope shape design in early stages of the design process: Integrating architectural design systems and energy simulation , 2013 .

[19]  Ibrahim Dincer,et al.  EXERGY ANALYSIS OF PSYCHROMETRIC PROCESSES , 2007 .

[20]  Carlos Barrios,et al.  Transformations on Parametric Design Models , 2005 .

[21]  David Jason Gerber,et al.  Designing-in performance: A framework for evolutionary energy performance feedback in early stage design , 2014 .

[22]  Siaw Kiang Chou,et al.  Energy performance of residential buildings in Singapore , 2010 .

[23]  Robert Aish,et al.  Multi-level Interaction in Parametric Design , 2005, Smart Graphics.

[24]  Shen-Guan Shih,et al.  A Hybrid Approach of Dynamic Programming and Genetic Algorithm for Multi-criteria Optimization on Sustainable Architecture design , 2014 .

[25]  Siaw Kiang Chou,et al.  An ETTV-based approach to improving the energy performance of commercial buildings , 2010 .

[26]  Gregory J. Ward,et al.  The RADIANCE lighting simulation and rendering system , 1994, SIGGRAPH.

[27]  A. Malkawi,et al.  Optimizing building form for energy performance based on hierarchical geometry relation , 2009 .

[28]  Vítor Leal,et al.  Envelope-related energy demand: A design indicator of energy performance for residential buildings in early design stages , 2013 .

[29]  Carlos Duarte,et al.  SinBerBEST Technology Energy Assessment Report , 2016 .

[30]  Reinhard Radermacher,et al.  Separate Sensible and Latent Cooling , 2014 .

[31]  Kian Wee Chen Architectural Design Exploration of Low-Exergy (LowEx) Buildings in the Tropics , 2015 .

[32]  Luca Baldini,et al.  Evaluating and adapting low exergy systems with decentralized ventilation for tropical climates , 2013 .

[33]  D. Robinson,et al.  Irradiation modelling made simple: the cumulative sky approach and its applications , 2004 .

[34]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.