An efficient metamodel-based method to carry out multi-objective building performance optimizations

Abstract Nowadays, performing multi-objective optimizations of actual building designs is one of the most challenging problems of the building energy efficiency area. This paper aims to propose an efficient method to solve multi-objective optimization building performance problems using a novel metamodel-based approach. To this end, the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is dynamically coupled with artificial neural network (ANN) metamodels, which were previously trained with results of building performance simulations conducted using the EnergyPlusTM software. This new approach proposes an optimal way to generate the samples used to train and validate the ANN-based metamodels minimizing the total of building energy simulations necessary to train them, and guarantees accurate optimization results. To validate the strengths of the proposed method, it is applied to optimize the energy efficiency and thermal comfort of an actual dwelling in order to get the best trade-off (Pareto front) of the building between heating and cooling performance. This case study involves 12 of the more influential discrete and categorical design variables like roof types, external and internal wall types, solar orientation, solar absorptance, size and type of windows, and the dimension of external window shadings of this house among others, making a complex building performance optimization problem with more than 108 possibilities to choose. Furthermore, the results obtained are systematically compared and validated with the “true” Pareto front achieved using a simulate-based scheme which directly couples EnergyPlus program and NSGA-II algorithm. Results indicated that the presented method is able to reduce up to 75% the number of building energy simulations needed to find the Pareto front of an actual multi-objective building performance optimization problem, keeping a good accuracy of the results.

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

[2]  Enedir Ghisi,et al.  Residential building design optimisation using sensitivity analysis and genetic algorithm , 2016 .

[3]  Yaolin Lin,et al.  Design Optimization Considering Variable Thermal Mass, Insulation, Absorptance of Solar Radiation, and Glazing Ratio Using a Prediction Model and Genetic Algorithm , 2018 .

[4]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[5]  Jan Hensen,et al.  A new methodology for investigating the cost-optimality of energy retrofitting a building category , 2015 .

[6]  Philipp Geyer,et al.  Automated metamodel generation for Design Space Exploration and decision-making – A novel method supporting performance-oriented building design and retrofitting , 2014 .

[7]  Runze Li,et al.  Design and Modeling for Computer Experiments , 2005 .

[8]  Jan Hensen,et al.  Building Performance Simulation for Design and Operation , 2019 .

[9]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[10]  Aris Tsangrassoulis,et al.  Algorithms for optimization of building design: A review , 2014 .

[11]  Francesco Causone,et al.  Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II) , 2015 .

[12]  Jean-Louis Scartezzini,et al.  Machine learning methods to assist energy system optimization , 2019, Applied Energy.

[13]  Martin T. Hagan,et al.  Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[14]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[15]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

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

[17]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application , 2014 .

[18]  Víctor D. Fachinotti,et al.  Generation of typical meteorological years for the Argentine Littoral Region , 2016 .

[19]  Ana Paula Melo,et al.  A novel surrogate model to support building energy labelling system: A new approach to assess cooling energy demand in commercial buildings , 2016 .

[20]  Rasmus Lund Jensen,et al.  A comparison of six metamodeling techniques applied to building performance simulations , 2018 .

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

[22]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[23]  Yousef Mohammadi,et al.  Multi-objective optimization of building envelope design for life cycle environmental performance , 2016 .

[24]  Anh Tuan Nguyen,et al.  A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems , 2016 .

[25]  Philippe Rigo,et al.  A review on simulation-based optimization methods applied to building performance analysis , 2014 .

[26]  Ralph Evins,et al.  Surrogate modelling for sustainable building design – A review , 2019, Energy and Buildings.

[27]  Yi-Ping Chen,et al.  Multiobjective optimization using nondominated sorting genetic algorithm-II for allocation of energy conservation and renewable energy facilities in a campus , 2016 .

[28]  Russell V. Lenth,et al.  Some Practical Guidelines for Effective Sample Size Determination , 2001 .

[29]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[30]  Gerardo Maria Mauro,et al.  CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building , 2017 .

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

[32]  Gerardo Maria Mauro,et al.  Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach , 2017 .

[33]  Alessandro Prada,et al.  On the performance of meta-models in building design optimization , 2018 .

[34]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

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

[36]  Marc Parizeau,et al.  DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..

[37]  Mary Ann Piette,et al.  Energy retrofit analysis toolkits for commercial buildings: A review , 2015 .

[38]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[39]  Giuseppe Peter Vanoli,et al.  Optimization of building envelope design for nZEBs in Mediterranean climate: Performance analysis of residential case study , 2016 .

[40]  Facundo Bre,et al.  A computational multi-objective optimization method to improve energy efficiency and thermal comfort in dwellings , 2017 .

[41]  Staf Roels,et al.  Comparative study of metamodelling techniques in building energy simulation: Guidelines for practitioners , 2014, Simul. Model. Pract. Theory.