Unit-Based Optimization Approaches for the Thermal Design of Residential Buildings

Yıl 2024, Cilt: 44 Sayı: 2, 339 - 350, 01.11.2024

Sadık Yıgıt , Semih Caglayan

https://doi.org/10.47480/isibted.1416709

Öz

Building regulations, scarcity of energy, and climate change have forced designers to find energy-efficient design alternatives for the buildings. Current regulations focus solely on the total energy requirement of the building without considering the fact that the energy performance varies greatly across different units of the building, which, in turn, causes discomfort among the occupants. Conventional optimization approaches created based on these regulations, therefore, miss the capability to cope with this issue. Resolving the problem of varying thermal performance within the units requires the introduction of unit-based optimization approaches. This study elaborates on revealing the inadequacy of the conventional optimization approach and proposes two alternative approaches that take the issue into account. Within this context, the thermal design a typical five-story residential building with six apartment units on each floor was optimized according to the conventional optimization approach. A simulation-based optimization system consisting of a Distributed Evolutionary Algorithms in Python (DEAP) optimization tool and Energy Plus was employed. The differences in the energy performances of different units were observed for three different climate conditions. Afterwards, two different approaches having the objectives of optimizing the overall building performance and balancing the variance within units were proposed: (i) single-phase multi-objective optimization and (ii) multi-phase single-objective optimization. The outcomes of the study demonstrated that the multi-phase single-objective optimization provided better results.

Anahtar Kelimeler

Unit-Based Optimization, Thermal Performance, Genetic Algorithm, Energy Efficiency, Climate Change.

Konut Binalarının Termal Tasarımı için Birim Tabanlı Optimizasyon Yaklaşımları

Öz

Yapı yönetmelikleri, kısıtlı enerji kaynakları ve iklim değişikliği, tasarımcıları binalar için enerji verimli tasarım alternatifleri bulmaya zorlamaktadır. Mevcut yönetmelikler, binanın toplam enerji ihtiyacına odaklanmakta olup, binanın farklı birimlerinde enerji performansının büyük ölçüde değişebileceği gerçeğini dikkate almamaktadır. Bu durum da bina sakinleri arasında rahatsızlık yaratmaktadır. Yönetmeliklere dayalı olarak oluşturulan geleneksel optimizasyon yaklaşımları, bu sorunla başa çıkmakta yetersiz kalmaktadır. Birimler arası değişen termal performans sorununu çözmek, birim tabanlı optimizasyon yaklaşımlarının kullanılmasını gerektirir. Bu çalışma, geleneksel optimizasyon yaklaşımının yetersizliğini ortaya koymakta ve bu sorunu dikkate alan iki alternatif yaklaşım önermektedir. Bu bağlamda, her katında altı daire bulunan tipik bir beş katlı konut binasının termal tasarımı, geleneksel optimizasyon yaklaşımına göre optimize edilmiştir. Bir optimizasyon aracı olan Distributed Evolutionary Algorithms in Python (DEAP) ve Energy Plus'tan oluşan bir simülasyon tabanlı optimizasyon sistemi kullanılmıştır. Farklı birimlerin enerji performansındaki değişimler üç ayrı iklim bölgesi için gözlemlenmiştir. Sonrasında, genel bina performansını optimize etmeyi ve birimler arasındaki varyansı dengelemeyi amaçlayan iki farklı yaklaşım önerilmiştir: (i) tek aşamalı çoklu amaçlı optimizasyon ve (ii) çoklu aşamalı tek amaçlı optimizasyon. Çalışmanın sonuçları, çoklu aşamalı tek amaçlı optimizasyonun daha iyi sonuçlar verdiğini göstermi.

Anahtar Kelimeler

Birim Tabanlı Optimizasyon, Termal Performans, Genetik Algoritma, Enerji Verimliliği, İklim Değişikliği

Kaynakça

• Al-Saadi, S. N. & Al-Jabri, K. S. (2020). Optimization of envelope design for housing in hot climates using a genetic algorithm (GA) computational approach. Journal of Building Engineering, 32, 101712.
• Anupong, W., Muda, I., AbdulAmeer, S. A., Al-Kharsan, I. H., Alviz-Meza, A., & Cárdenas-Escrocia, Y. (2023). Energy consumption and carbon dioxide production optimization in an educational building using the supported vector machine and ant colony system. Sustainability, 15(4), 3118.
• Asadi, E., da Silva, M. G., Antunes, C. H., & Dias, L. (2012). A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB. Building and Environment, 56, 370-378.
• Ascione, F., Bianco, N., De Masi, R. F., Mauro, G. M., & Vanoli, G. P. (2015). Design of the building envelope: A novel multi-objective approach for the optimization of energy performance and thermal comfort. Sustainability, 7(8), 10809-10836.
• Ascione, F., Bianco, N., De Masi, R. F., Mauro, G. M., & Vanoli, G. P. (2017). Energy retrofit of educational buildings: Transient energy simulations, model calibration and multi-objective optimization towards nearly zero-energy performance. Energy and Buildings, 144, 303-319.
• Ascione, F., De Masi, R. F., de Rossi, F., Ruggiero, S., & Vanoli, G. P. (2016). Optimization of building envelope design for nZEBs in Mediterranean climate: Performance analysis of residential case study. Applied Energy, 183, 938-957.
• Bamdad, K., Cholette, M. E., Guan, L., & Bell, J. (2017). Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms. Energy and Buildings, 154, 404-414.
• Barber, K. A. & Krarti, M. (2022). A review of optimization based tools for design and control of building energy systems. Renewable and Sustainable Energy Reviews, 160, 112359.
• Bertoldi, P. (2022). Policies for energy conservation and sufficiency: Review of existing policies and recommendations for new and effective policies in OECD countries. Energy and Buildings, 264, 112075.
• Bigot, D., Miranville, F., Boyer, H., Bojic, M., Guichard, S., & Jean, A. (2013). Model optimization and validation with experimental data using the case study of a building equipped with photovoltaic panel on roof: Coupling of the building thermal simulation code ISOLAB with the generic optimization program GenOpt. Energy and Buildings, 58, 333-347.
• Buonomano, A., Barone, G., & Forzano, C. (2022). Advanced energy technologies, methods, and policies to support the sustainable development of energy, water and environment systems. Energy Reports, 8, 4844-4853.
• Caglayan, S., Ozorhon, B., Ozcan-Deniz, G., & Yigit, S. (2020a). A life cycle costing approach to determine the optimum insulation thickness of existing buildings. Journal of Thermal Science and Technology, 40(1), 1-14.
• Caglayan, S., Yigit, S., Ozorhon, B., & Ozcan-Deniz, G. (2020b). A genetic algorithm-based envelope design optimisation for residential buildings. Proceedings of the Institution of Civil Engineers - Engineering Sustainability, 173(6), 280-290.
• Caglayan, S., Ozorhon, B., & Kurnaz, L. (2022). Nationwide mapping of optimum wall insulation thicknesses: A stochastic approach. Journal of Thermal Science and Technology, 42(2), 169-202.
• Ciardiello, A., Rosso, F., Dell'Olmo, J., Ciancio, V., Ferrero, M., & Salata, F. (2020). Multi-objective approach to the optimization of shape and envelope in building energy design. Applied Energy, 280, 115984.
• Delgarm, N., Sajadi, B., Kowsary, F., & Delgarm, S. (2016). Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Applied Energy, 170, 293-303.
• EnergyPlus (2020, October 30). Turkish Weather for Energy Calculations (TWEC) for Energy Plus. https://energyplus.net/weather-region/europe_wmo_region_6/TUR
• Eskander, M. M., Sandoval-Reyes, M., Silva, C. A., Vieira, S. M., & Sousa, J. M. (2017). Assessment of energy efficiency measures using multi-objective optimization in Portuguese households. Sustainable Cities and Society, 35, 764-773.
• Ferrara, M., Fabrizio, E., Virgone, J., & Filippi, M. (2014). A simulation-based optimization method for cost-optimal analysis of nearly Zero Energy Buildings. Energy and Buildings, 84, 442-457.
• Fortin, F. A., Rainville, F. M. D., Gardner, M. A., Parizeau, M., & Gagné, C. (2012). DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13, 2171-2175.
• Gao, H., Wang, X., Wu, K., Zheng, Y., Wang, Q., Shi, W., & He, M. (2023). A review of building carbon emission accounting and prediction models. Buildings, 13(7), 1617.
• Ge, J., Wu, J., Chen, S., & Wu, J. (2018). Energy efficiency optimization strategies for university research buildings with hot summer and cold winter climate of China based on the adaptive thermal comfort. Journal of Building Engineering, 18, 321-330.
• Ghalambaz, M., Yengejeh, R. J., & Davami, A. H. (2021). Building energy optimization using grey wolf optimizer (GWO). Case Studies in Thermal Engineering, 27, 101250.
• Ghafoori, M. & Abdallah, M. (2022). Simulation-based optimization model to minimize equivalent annual cost of existing buildings. Journal of Construction Engineering and Management, 148(2), 04021202.
• Gilles, F., Bernard, S., Ioannis, A., & Simon, R. (2017). Decision-making based on network visualization applied to building life cycle optimization. Sustainable Cities and Society, 35, 565-573.
• Griego, D., Krarti, M., & Hernandez-Guerrero, A. (2015). Energy efficiency optimization of new and existing office buildings in Guanajuato, Mexico. Sustainable Cities and Society, 17, 132-140.
• He, Y., Chu, Y., Song, Y., Liu, M., Shi, S., & Chen, X. (2022). Analysis of design strategy of energy efficient buildings based on databases by using data mining and statistical metrics approach. Energy and Buildings, 258, 111811.
• Huang, Y. & Niu, J. L. (2016). Optimal building envelope design based on simulated performance: History, current status and new potentials. Energy and Buildings, 117, 387-398.
• Jin, Q., Favoino, F., & Overend, M. (2017). Design and control optimisation of adaptive insulation systems for office buildings. Part 2: A parametric study for a temperate climate. Energy, 127, 634-649.
• Junghans, L. & Darde, N. (2015). Hybrid single objective genetic algorithm coupled with the simulated annealing optimization method for building optimization. Energy and Buildings, 86, 651-662.
• Kaya, R. & Caglayan, S. (2023). Potential benefits of thermal insulation in public buildings: Case of a university building. Buildings, 13(10), 2586.
• Khan, F. A., Ullah, K., ur Rahman, A., & Anwar, S. (2023). Energy optimization in smart urban buildings using bio-inspired ant colony optimization. Soft Computing, 27(2), 973-989.
• Kheiri, F. (2021). Optimization of building fenestration and shading for climate-based daylight performance using the coupled genetic algorithm and simulated annealing optimization methods. Indoor and Built Environment, 30(2), 195-214.
• Kim, H. & Clayton, M. J. (2020). A multi-objective optimization approach for climate-adaptive building envelope design using parametric behavior maps. Building and Environment, 185, 107292.
• Kontoleon, K. J. & Eumorfopoulou, E. A. (2010). The effect of the orientation and proportion of a plant-covered wall layer on the thermal performance of a building zone. Building and Environment, 45(5), 1287-1303.
• Lee, D. Y., Seo, B. M., Yoon, Y. B., Hong, S. H., Choi, J. M., & Lee, K. H. (2019). Heating energy performance and part load ratio characteristics of boiler staging in an office building. Frontiers in Energy, 13(2), 339-353.
• Li, L., Fu, Y., Fung, J. C., Qu, H., & Lau, A. K. (2021). Development of a back-propagation neural network and adaptive grey wolf optimizer algorithm for thermal comfort and energy consumption prediction and optimization. Energy and Buildings, 253, 111439.
• Li, S., Liu, L., & Peng, C. (2020). A review of performance-oriented architectural design and optimization in the context of sustainability: Dividends and challenges. Sustainability, 12(4), 1427.
• Lin, Y. & Yang, W. (2018). Application of multi-objective genetic algorithm based simulation for cost-effective building energy efficiency design and thermal comfort improvement. Frontiers in Energy Research, 6, 25.
• Longo, S., Montana, F., & Sanseverino, E. R. (2019). A review on optimization and cost-optimal methodologies in low-energy buildings design and environmental considerations. Sustainable Cities and Society, 45, 87-104.
• Malinauskaite, J., Jouhara, H., Ahmad, L., Milani, M., Montorsi, L., & Venturelli, M. (2019). Energy efficiency in industry: EU and national policies in Italy and the UK. Energy, 172, 255-269.
• MENR (2022). Republic of Türkiye Ministry of Energy and Natural Resources. Türkiye National Energy Plan.
• Naderi, E., Sajadi, B., Behabadi, M. A., & Naderi, E. (2020). Multi-objective simulation-based optimization of controlled blind specifications to reduce energy consumption, and thermal and visual discomfort: Case studies in Iran. Building and Environment, 169, 106570.
• Niemelä, T., Kosonen, R., & Jokisalo, J. (2016). Cost-optimal energy performance renovation measures of educational buildings in cold climate. Applied Energy, 183, 1005-1020.
• Ozel, M. (2022). Determination of indoor design temperature, thermal characteristics and insulation thickness under hot climate conditions. Journal of Thermal Science and Technology, 42(1), 49-64.
• Perera, D. W. U., Winkler, D., & Skeie, N. O. (2016). Multi-floor building heating models in MATLAB and Modelica environments. Applied Energy, 171, 46-57.
• Pooyanfar, M. & Topal, H. (2018). Assessing effectiveness of integrated building design parameters on energy performance and emissions in health care facilities by means of building energy modelling. Journal of Thermal Science and Technology, 38(2), 151-165.
• Ren, H., Lu, Y., Wu, Q., Yang, X., & Zhou, A. (2018). Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm. Frontiers in Energy, 12(4), 518-528.
• Sharif, S. A. & Hammad, A. (2019). Simulation-based multi-objective optimization of institutional building renovation considering energy consumption, life-cycle cost and life-cycle assessment. Journal of Building Engineering, 21, 429-445.
• Si, B., Tian, Z., Jin, X., Zhou, X., Tang, P., & Shi, X. (2016). Performance indices and evaluation of algorithms in building energy efficient design optimization. Energy, 114, 100-112.
• Somu, N., MR, G. R., & Ramamritham, K. (2020). A hybrid model for building energy consumption forecasting using long short term memory networks. Applied Energy, 261, 114131.
• Song, X., Ye, C., Li, H., Wang, X., & Ma, W. (2017). Field study on energy economic assessment of office buildings envelope retrofitting in southern China. Sustainable Cities and Society, 28, 154-161.
• Ucar, A. (2024). The annual CO2 emissions and energy costs of different exterior wall structures in residential buildings in Türkiye. Journal of Thermal Science and Technology, 44(1), 1-17.
• Wang, R., Lu, S., & Feng, W. (2020). A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost. Energy, 192, 116723.
• Wang, Z. L., Funada, T., Onda, T., & Chen, Z. C. (2023). Knowledge extraction and performance improvement of Bi2Te3-based thermoelectric materials by machine learning. Materials Today Physics, 31, 100971.
• Yigit, S. (2021). A machine-learning-based method for thermal design optimization of residential buildings in highly urbanized areas of Turkey. Journal of Building Engineering, 38, 102225.
• Yigit, S. & Ozorhon, B. (2018). A simulation-based optimization method for designing energy efficient buildings. Energy and Buildings, 178, 216-227.
• Yu, J., Yang, C., & Tian, L. (2008). Low-energy envelope design of residential building in hot summer and cold winter zone in China. Energy and Buildings, 40(8), 1536-1546.
• Yu, L., Qin, S., Zhang, M., Shen, C., Jiang, T., & Guan, X. (2021). A review of deep reinforcement learning for smart building energy management. IEEE Internet of Things Journal, 8(15), 12046-12063.
• Yu, Z. J., Chen, J., Sun, Y., & Zhang, G. (2016). A GA-based system sizing method for net-zero energy buildings considering multi-criteria performance requirements under parameter uncertainties. Energy and Buildings, 129, 524-534.
• Yue, N., Li, L., Morandi, A., & Zhao, Y. (2021). A metamodel-based multi-objective optimization method to balance thermal comfort and energy efficiency in a campus gymnasium. Energy and Buildings, 253, 111513.
• Zhou, G., Moayedi, H., Bahiraei, M., & Lyu, Z. (2020). Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. Journal of Cleaner Production, 254, 120082.
• Zhou, Z., Wang, C., Sun, X., Gao, F., Feng, W., & Zillante, G. (2018). Heating energy saving potential from building envelope design and operation optimization in residential buildings: A case study in northern China. Journal of Cleaner Production, 174, 413-423.
• Zune, M., Pantua, C. A. J., Rodrigues, L., & Gillott, M. (2020). A review of traditional multistage roofs design and performance in vernacular buildings in Myanmar. Sustainable Cities and Society, 60, 102240.