Parallel Computing Framework for Optimizing Environmental and Economic Performances of Housing Units

AbstractDecision makers in the housing industry need to carefully analyze housing design and construction decisions to improve housing environmental and economic performances. Available energy optimization models are able to find minimum-cost housing design and construction decisions at different target energy-saving levels. The application of these models, however, is limited due to their time-intensive and often impractical computational requirements. This paper presents a scalable and expandable parallel computing framework to reduce the computational time that is required to optimize the trade-offs between the environmental performance of housing units and their initial cost. The framework is designed as a global parallel optimization algorithm to provide an efficient distribution of the multiobjective genetic algorithm computations over a number of parallel processors. The optimization algorithm is also coupled with an external building energy simulation engine to enable an accurate modeling of housi...

[1]  Moncef Krarti,et al.  Design optimization of energy efficient residential buildings in Tunisia , 2012 .

[2]  Alan H. Karp,et al.  Measuring parallel processor performance , 1990, CACM.

[3]  Filip Kulic,et al.  HVAC system optimization with CO2 concentration control using genetic algorithms , 2009 .

[4]  Nikolaos Ploskas,et al.  Parallel Computing Toolbox , 2016 .

[5]  D. Arasteh,et al.  Glazing energy performance and design optimization with daylighting , 1984 .

[6]  Wenyan Wu,et al.  Accounting for Greenhouse Gas Emissions in Multiobjective Genetic Algorithm Optimization of Water Distribution Systems , 2010 .

[7]  Leslie K. Norford,et al.  Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems , 2003 .

[8]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[9]  Philip Fairey,et al.  Updated Miscellaneous Electricity Loads and Appliance Energy Usage Profiles for Use in Home Energy Ratings, the Building America Benchmark Procedures and Related Calculations. Revised , 2011 .

[10]  Khaled A El-Rayes,et al.  Parallel computing framework for optimizing construction planning in large-scale projects , 2005 .

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

[12]  Ishfaq Ahmad,et al.  Efficient Scheduling of Arbitrary TAsk Graphs to Multiprocessors Using a Parallel Genetic Algorithm , 1997, J. Parallel Distributed Comput..

[13]  S. C. Kaushik,et al.  Dynamic earth-contact building: A sustainable low-energy technology , 2007 .

[14]  Viktor Dorer,et al.  Energy and CO2 emissions performance assessment of residential micro-cogeneration systems with dynamic whole-building simulation programs , 2009 .

[15]  Jonathan A. Wright,et al.  Optimization of building thermal design and control by multi-criterion genetic algorithm , 2002 .

[16]  Weimin Wang,et al.  Applying multi-objective genetic algorithms in green building design optimization , 2005 .

[17]  David E. Goldberg,et al.  Designing a competent simple genetic algorithm for search and optimization , 2000 .

[18]  Moncef Krarti,et al.  Enhanced Sequential Search Methodology for Identifying Cost-Optimal Building Pathways , 2008 .

[19]  Barbara C. Lippiatt,et al.  Energy Price Indices and Discount Factors for Life-Cycle Cost Analysis – 2012: Annual Supplement to NIST Handbook 135 and NBS Special Publication 709 , 2012 .

[20]  Moncef Krarti,et al.  Genetic-algorithm based approach to optimize building envelope design for residential buildings , 2010 .

[21]  Khaled A El-Rayes,et al.  Parallel Genetic Algorithms for Optimizing Resource Utilization in Large-Scale Construction Projects , 2006 .

[22]  G. Keoleian,et al.  Life‐Cycle Energy, Costs, and Strategies for Improving a Single‐Family House , 2000 .

[23]  David E. Goldberg,et al.  Predicting Speedups of Ideal Bounding Cases of Parallel Genetic Algorithms , 1997, ICGA.

[24]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[25]  Robert Hendron,et al.  Building America House Simulation Protocols , 2010 .

[26]  S. Rushing,et al.  Energy Price Indices and Discount Factors for Life-Cycle Cost Analysis - April 2006 , 1997 .

[27]  Enrique Alba,et al.  Parallel Genetic Algorithms , 2011, Studies in Computational Intelligence.

[28]  Stephen R. Petersen,et al.  Life-cycle costing manual for the Federal Energy Management Program , 1996 .