A Comparison of Three Evolutionary Algorithms for the Optimization of Building Design

Abstract:

In building design, the need of optimization algorithms is considerably increasing due to the requirements of enhancing the overall performances, cost and sustainability objectives. An evolutionary algorithm coupled with building simulation code is often used. However, this approach is not widespread in actual application due to the high number of expensive simulation runs required by evolutionary algorithms. For this reason, the selection of an efficient and effective optimization algorithm becomes a key aspect in building design. In the literature there are several works analyzing the performance of different optimization algorithms, most of them by comparing the results obtained for the optimization of analytical test functions. However, there are no evidence-based studies deepening the efficiency and efficacy of these methods by comparing against the true solution of the discrete optimization problems on building design. This study compares the number of cost function evaluations and the percentage of the actual Pareto solutions of three algorithms used for the evaluation of the optimal refurbishment of three reference buildings for which the actual Pareto front is known through a brute-force approach.

• Periodical: Applied Mechanics and Materials (Volume 887)
• Pages: 140-147
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.887.140
• Online since: January 2019
• Authors: Alessandro Prada*, Andrea Gasparella, Paolo Baggio
• Keywords: Building Energy Simulation, Building Refurbishment, Cost Optimal, Genetic Algorithm, Multi-Objective Optimization

References

[1] European Commission. Directive 2010/31/EU of the European Parliament and of the council of 19th May 2010 on the energy performance of buildings (recast). Official Journal of European Union 153 (2010) 13 – 35.

[2] E. Carlon, M. Schwarz, A. Prada, L. Golicza, V. K. Verma, M. Baratieri, W. Haslinger, and C. Schmidl, On-site monitoring and dynamic simulation of a low energy house heated by a pellet boiler. Energy and Buildings 116 (2016) p.296–306.

DOI: 10.1016/j.enbuild.2016.01.001

[3] A.T. Nguyen, S. Reiter, P. Rigo, A review on simulation-based optimization methods applied to building performance analysis, Applied Energy 113 (2014) pp.1043-1058.

DOI: 10.1016/j.apenergy.2013.08.061

[4] K. Deb, A. Pratap, S. Agarwal, T.A.M.T. Meyarivan, A fast and elitist multiobjective genetic algorithm: nSGA-II, IEEE Transaction on Evolutionary Computation 6 (2002) p.182–197.

DOI: 10.1109/4235.996017

[5] E. Zitzler and L. Thiele, Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on evolutionary computation 3 (1999) p.257–271.

DOI: 10.1109/4235.797969

[6] K. Deb and H. Jain, An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18 (2014) p.577–601.

DOI: 10.1109/tevc.2013.2281535

[7] K. Deb and H. Jain, An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach part II: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation, 18 (2014) p.602–622.

DOI: 10.1109/tevc.2013.2281534

[8] Q. Zhang and H. Li, MOEA-D: A multi-objective evolutionary algorithm based on decomposition, IEEE Transactions on Evolutionary Computation, 11 (2007) p.712–731.

DOI: 10.1109/tevc.2007.892759

[9] P. Penna, A. Prada, F. Cappelletti, and A. Gasparella. Multi-objectives optimization of energy efficiency measures in existing buildings. Energy and Buildings 95 (2015) p.57–69.

DOI: 10.1016/j.enbuild.2014.11.003

[10] ASHRAE 90.1 Energy standard for buildings except low rise residential buildings. Technical report, ASHRAE - American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Atlanta, Georgia (2007).

DOI: 10.2172/5737921

[11] Haller MY, Paavilainen J, Konersmann L, Haberl R, Dröscher A, Frank E, et al. A unified model for the simulation of oil, gas and biomass space heating boilers for energy estimating purposes. Part I: model development. Journal of Building Performance Simulation 4 (2011) p.1–18.

DOI: 10.1080/19401491003671944

[12] M.Y. Haller, J. Paavilainen, L. Konersmann, R. Haberl, A. Dröscher, E. Frank, C. Bales, W. Streicher, A unified model for the simulation of oil, gas and biomass space heating boilers for energy estimating purposes Part II: parameterization and comparison with measurements, Journal of Building Performance Simulation 4 (2011) p.19–36.

DOI: 10.1080/19401491003653629

[13] UNI/TS 11300-1, Energy performance of buildings – Part 1: Evaluation of energy need for space heating and cooling, UNI, (2008).

[14] European Commission, Commission Delegated Regulation (EU) No 244/2012 of 16 January 2012 supplementing Directive 2010/31/EU, (2012).

[15] EN 15459, Energy performance of buildings – Economic evaluation procedure for energy systems in buildings. CEN (2009).

DOI: 10.3403/30344395