A Study on Development of a Cost Optimal and Energy Saving Building Model: Focused on Industrial Building

This study suggests an optimization method for the life cycle cost (LCC) in an economic feasibility analysis when applying energy saving techniques in the early design stage of a building. Literature and previous studies were reviewed to select appropriate optimization and LCC analysis techniques. The energy simulation (Energy Plus) and computational program (MATLAB) were linked to provide an automated optimization process. From the results, it is suggested that this process could outline the cost optimization model with which it is possible to minimize the LCC. To aid in understanding the model, a case study on an industrial building was performed to outline the operations of the cost optimization model including energy savings. An energy optimization model was also presented to illustrate the need for the cost optimization model.

[1]  René Oly,et al.  Air infiltration assessment for industrial buildings , 2015 .

[2]  Wei Pan,et al.  Relationships between air-tightness and its influencing factors of post-2006 new-build dwellings in the UK , 2010 .

[3]  David Coley,et al.  Low-energy design: combining computer-based optimisation and human judgement , 2002 .

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

[5]  Luis C. Dias,et al.  A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB , 2012 .

[6]  William W. Braham,et al.  An integrated energy–emergy approach to building form optimization: Use of EnergyPlus, emergy analysis and Taguchi-regression method , 2015 .

[7]  Michael John Kuchta Daylighting in American industrial architecture: Three investigations , 1994 .

[8]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[9]  Young-Jin Kim,et al.  Optimal Design of Residential Ventilation Systems using Integration of Genetic Algorithm, Pareto Optimality and CONTAMW 2.4 , 2008 .

[10]  Targo Kalamees,et al.  Building leakage, infiltration, and energy performance analyses for Finnish detached houses , 2009 .

[11]  Michael D. Sohn,et al.  Analyzing a database of residential air leakage in the United States , 2005 .

[12]  Qingyan Chen,et al.  Experimental and Simulation Study on the Performance of Daylighting in an Industrial Building and its Energy Saving Potential , 2014 .

[13]  Jiangjiang Wang,et al.  Review on multi-criteria decision analysis aid in sustainable energy decision-making , 2009 .

[14]  Francesco Asdrubali,et al.  Daylighting performance of sawtooth roofs of industrial buildings , 2003 .

[15]  Dionysia Kolokotsa,et al.  On the cooling potential of cool roofs in cold climates: Use of cool fluorocarbon coatings to enhance the optical properties and the energy performance of industrial buildings , 2014 .

[16]  Xiaoxin Wang,et al.  A case study on energy consumption and overheating for a UK industrial building with rooflights , 2013 .

[17]  Martin Lopušniak,et al.  Analysis of thermal energy demand and saving in industrial buildings: A case study in Slovakia , 2013 .

[18]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[19]  Christoph Weber,et al.  Energy Efficiency in Innovative Industries: Application and Benefits of Energy Indicators in the Automobile Industry , 2009 .

[20]  James F. Munce Industrial architecture: an analysis of international building practice , 1960 .

[21]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[22]  Ala Hasan,et al.  Applying a multi-objective optimization approach for Design of low-emission cost-effective dwellings , 2011 .

[23]  Jonathan A. Wright,et al.  A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization , 2004 .

[24]  J. C. Lam,et al.  Measurements of solar radiation and illuminance on vertical surfaces and daylighting implications , 2000 .

[25]  Richard L. Ottinger,et al.  Compendium of Sustainable Energy Laws: Explanatory Memorandum and Proposal for a Directive of the European Parliament and of the Council on the Energy Performance of Buildings , 2005 .

[26]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[27]  M. J. Jiménez,et al.  Energetic analysis of a passive solar design, incorporated in a courtyard after refurbishment, using an innovative cover component based in a sawtooth roof concept , 2005 .

[28]  Yong Wang,et al.  A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization , 2006, IEEE Transactions on Evolutionary Computation.

[29]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[30]  M Paroncini,et al.  Comment on ‘Daylighting performance of sawtooth roofs of industrial buildings’ by F Asdrubali , 2003 .

[31]  Henry P. Wynn,et al.  Quality through design : experimental design, off-line quality control and Taguchi's contributions , 1991 .

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