OPTIMIZING THERMAL COMFORT AND ENERGY CONSUMPTION IN A LARGE BUILDING WITHOUT RENOVATION WORK

This paper proposes a new methodology to reduce energy consumptions in large buildings while simultaneously optimizing thermal comfort. The model designed with an energy simulation program is calibrated by the Covariance Matrix Adaptation Evolutionary Strategy using observations including consumptions, inside temperatures and comfort measurements such as CO2 emissions obtained with sensors displayed in the building. The temperatures inside the building and the energy consumptions predicted by the calibrated model during a new time period are then compared to the corresponding observations. The model is then used to find a set of Pareto optimal schedulings and tunings of the building management system in terms of energy loads and thermal comfort using multi-objective optimization.

[1]  Stefan Roth,et al.  Covariance Matrix Adaptation for Multi-objective Optimization , 2007, Evolutionary Computation.

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

[3]  Mohamed El Mankibi,et al.  Development of a multicriteria tool for optimizing the renovation of buildings , 2011 .

[4]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

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

[6]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

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

[8]  Rudai Shan,et al.  Optimization for Heating, Cooling and Lighting Load in Building Façade Design , 2014 .

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

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

[11]  Khee Poh Lam,et al.  Coupling of whole-building energy simulation and multi-dimensional numerical optimization for minimizing the life cycle costs of office buildings , 2014 .

[12]  D. Gossard,et al.  Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network , 2013 .

[13]  Farshad Kowsary,et al.  Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO) , 2016 .

[14]  R. Hendron.  Building America Research Benchmark Definition , 2008 .

[15]  John S. Gero,et al.  Tradeoff diagrams for the integrated design of the physical environment in buildings , 1980 .

[16]  Ertunga C. Özelkan,et al.  Optimizing complex building design for annual daylighting performance and evaluation of optimization algorithms , 2015 .

[17]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[18]  M. Adamski.  Optimization of the form of a building on an oval base , 2007 .

[19]  Wojciech Marks,et al.  MULTICRITERIA OPTIMISATION OF SHAPE OF ENERGY-SAVING BUILDINGS , 1997 .