An ANN-exhaustive-listing method for optimization of multiple building shapes and envelope properties with maximum thermal performance

Abstract

With increasing awareness of sustainability, demands on optimized design of building shapes with a view to maximize its thermal performance have become stronger. Current research focuses more on building envelopes than shapes, and thermal comfort of building occupants has not been considered in maximizing thermal performance in building shape optimization. This paper attempts to develop an innovative ANN (artificial neural network)-exhaustive-listing method to optimize the building shapes and envelope physical properties in achieving maximum thermal performance as measured by both thermal load and comfort hour. After verified, the developed method is applied to four different building shapes in five different climate zones in China. It is found that the building shape needs to be treated separately to achieve sufficient accuracy of prediction of thermal performance and that the ANN is an accurate technique to develop models of discomfort hour with errors of less than 1.5%. It is also found that the optimal solutions favor the smallest window-to-external surface area with triple-layer low-E windows and insulation thickness of greater than 90 mm. The merit of the developed method is that it can rapidly reach the optimal solutions for most types of building shapes with more than two objective functions and large number of design variables.

Graphical abstract

Keywords

ANN (artificial neural network) / exhaustive-listing / building shape / optimization / thermal load / thermal comfort

References

[1] Depecker P, Menezo C, Virgone J, Lepers S. Design of buildings shape and energetic consumption. Building and Environment, 2001, 36(5): 627–635

[2] Aksoy U T, Inalli M. Impacts of some building passive design parameters on heating demand for a cold region. Building and Environment, 2006, 41(12): 1742–1754

[3] Choi I Y, Cho S H, Kim J T. Energy consumption characteristics of high-rise apartment buildings according to building shape and mixed-use development. Energy and Building, 2012, 46: 123–131

[4] Premrov M, Žegarac Leskovar V, Mihalič K. Influence of the building shape on the energy performance of timber-glass buildings in different climatic conditions. Energy, 2016, 108: 201–211

[5] Okeil A. A holistic approach to energy efficient building forms. Energy and Building, 2010, 42(9): 1437–1444

[6] Yaşa E, Ok V.. Evaluation of the effects of courtyard building shapes on solar heat gains and energy efficiency according to different climatic regions. Energy and Building, 2014, 73: 192–199 doi:10.1016/j.enbuild.2013.12.042

[7] AlAnzi A, Seo D, Krarti M. Impact of building shape on thermal performance of office buildings in Kuwait. Energy Conversion and Management, 2009, 50(3): 822–828

[8] Ourghi R, Al-Anzi A, Krarti M. A simplified analysis method to predict the impact of shape on annual energy use for office buildings. Energy Conversion and Management, 2007, 48(1): 300–305

[9] Marks W. Multicriteria optimisation of shape of energy-saving buildings. Building and Environment, 1997, 32(4): 331–339

[10] Kämpf J H, Robinson D. Optimisation of building form for solar energy utilisation using constrained evolutionary algorithms. Energy and Building, 2010, 42(6): 807–814

[11] Caruso G, Fantozzi F, Leccese F. Optimal theoretical building form to minimize direct solar irradiation. Solar Energy, 2013, 97: 128–137

[12] Jin J, Jeong J. Thermal characteristic prediction models for a free-form building in various climate zones. Energy, 2013, 50: 468–476

[13] Wang W, Rivard H, Zmeureanu R. Floor shape optimization for green building design. Advanced Engineering Informatics, 2006, 20(4): 363–378

[14] Yi Y K, Malkawi A M. Optimizing building form for energy performance based on hierarchical geometry relation. Automation in Construction, 2009, 18(6): 825–833

[15] Tuhus-Dubrow D, Krarti M. Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment, 2010, 45(7): 1574–1581

[16] Jin J, Jeong J. Optimization of a free-form building shape to minimize external thermal load using genetic algorithm. Energy and Building, 2014, 85: 473–482

[17] Zhang L, Zhang L, Wang Y. Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China. Solar Energy, 2016, 132: 38–50

[18] Jedrzejuk H, Marks W. Optimization of shape and functional structure of buildings as well as heat source utilisation example. Building and Environment, 2002, 37(12): 1249–1253

[19] Caruso G, Kämpf J H. Building shape optimisation to reduce air-conditioning needs using constrained evolutionary algorithms. Solar Energy, 2015, 118: 186–196

[20] Adamski M. Optimization of the form of a building on an oval base. Building and Environment, 2007, 42(4): 1632–1643

[21] Fokaides P A, Panayidou A, Hadjichristos C, Phocas M C. Application of non-linear programming to optimize buildings’ solar exposure. Journal of Building Engineering, 2017, 11: 127–133

[22] Magnier L, Haghighat F. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment, 2010, 45(3): 739–746

[23] Li J, Yin S W, Shi G S, Wang L. Optimization of indoor thermal comfort parameters with the adaptive network-based fuzzy inference system and particle swarm optimization algorithm. Mathematical Problems in Engineering, 2017: 3075432

[24] Mushtaha E, Helmy O. Impact of building forms on thermal performance and thermal comfort conditions in religious buildings in hot climates: a case study in Sharjah city. International Journal of Sustainable Energy, 2017, 36(10): 926–944

[25] Gou S, Nik V M, Scartezzini J L, Zhao Q, Li Z. Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand. Energy and Building, 2018, 169: 484–506

[26] Debnath B K, Das R. Prediction of performance coefficients of a three-bucket Savonius rotor using artificial neural network. Journal of Renewable and Sustainable Energy, 2010, 2(4): 043107

[27] Das R, Prasad D K. Prediction of porosity and thermal diffusivity in a porous fin using differential evolution algorithm. Swarm and Evolutionary Computation, 2015, 23: 27–39

[28] Das R, Akay B, Singla R K, Singh K. Application of artificial bee colony algorithm for inverse modelling of a solar collector. Inverse Problems in Science and Engineering, 2017, 25(6): 887–908

[29] Das R, Singh K, Akay B, Gogoi T K. Application of artificial bee colony algorithm for maximizing heat transfer in a perforated fin. Proceedings of the Institution of Mechanical Engineers. Part E, Journal of Process Mechanical Engineering, 2018, 232(1): 38–48

[30] DesignBuilder. Simulation made easy. 2017–07–05,

[31] EnergyPlus. Testing and Validation. 2018–05,

[32] Coakley D, Corry E J, Keane D M. Validation of simulated thermal comfort in the context of building performance evaluation & optimisation. In: Proceedings of the 13th International Conference for Enhanced Building Operations, Montreal, Quebec, Canada, 2013

[33] DesignBuilder. Tests for the calculation of energy use for space heating and cooling. 2017–08–05,

[34] Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Zone. Beijing: China Architectural Engineering Industrial Publishing Press, 2010 (in Chinese)

[35] Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Warm Winter Zone. Beijing: China Architectural Engineering Industrial Publishing Press, 2012 (in Chinese)

[36] Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Design Standard for Energy Efficiency of Residential Buildings in Severe Cold and Cold Zones. Beijing: China Architectural Engineering Industrial Publishing Press, 2010 (in Chinese)

[37] Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Evaluation Standard for Indoor Thermal Environment in Civil Building. Beijing: China Architectural Engineering Industrial Publishing Press, 2012 (in Chinese)

[38] Fanger P O. Thermal Comfort. Copenhagen: Danish Technical Press, 1970

[39] Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Standard for Lighting Design of Buildings. Beijing: China Architectural Engineering Industrial Publishing Press, 2013 (in Chinese)

[40] Ministry of Housing and Urban-Rural Construction of the People’s Republic of China (MOHURD). Design Code for Design of Hating Ventilation and Air Conditioning of Civil Buildings. Beijing: China Architectural Engineering Industrial Publishing Press, 2012 (in Chinese)

[41] McKay M D. In: Ronen Y, ed. Sensitivity Arid Uncertainty Analysis Using a Statistical Sample of Input Values, Uncertainty Analysis. CRC Press, 1998, 145–186

[42] Zhou L, Haghighat F. Optimization of ventilation system design and operation in office environment, part I: methodology. Building and Environment, 2009, 44(4): 651–656

[43] Conraud J. A methodology for the optimization of building energy, thermal, and visual performance. Dissertation for Master’s Degree. Montreal: Concordia University, 2008

RIGHTS & PERMISSIONS

Higher Education Press