Analyzing occupancy density with directional effect to optimize energy consumption in a university building using the response surface method

Yıl 2025, Cilt: 40 Sayı: 2, 1059 - 1072

Resul Özlük , Yusuf Yıldız

https://doi.org/10.17341/gazimmfd.1455889

Öz

University buildings are generally buildings with high energy consumption. Therefore, university buildings play an important role in saving energy and reducing carbon emissions. In recent years, there has been a significant increase in the number of students and university buildings. This increase increases the importance of effective use of spaces. Mostly, university buildings are heated and cooled without paying attention to space layouts and occupancy rates. In this study, which aims to improve building energy efficiency with optimum occupancy rates in classrooms facing different directions, the RSM method integrating computational simulation and multi-objective optimization was used. In this study, building occupancy rates were first calculated for building simulation and design solutions were produced for multi-objective optimization in line with the calculations. One of the most important findings is that the occupancy rates of optimum solutions, where the desirability of energy consumption is high in different periods, differ in the morning and afternoon periods of the same period. With optimum solutions, energy savings of approximately 2% in heating consumption and 9.3% in cooling consumption were achieved. In addition, the most effective parameter for energy consumption for heating purposes was the northern occupancy rate, while the most effective parameter for energy consumption for cooling purposes was the western occupancy rate. Eastern occupancy rate was found to be the least effective parameter. This study aims to provide a new perspective on future research on space use and layout for energy saving in educational buildings.

Anahtar Kelimeler

Building occupancy, multi-objective optimization, response surface method, energy efficiency, use of space in educational buildings.

Yanıt yüzeyi yöntemi ile üniversite binasında enerji tüketimini optimize etmek için kullanıcı yoğunluğunun yön etkisiyle birlikte analiz edilmesi

Öz

Üniversite binaları, genellikle enerji tüketimi yüksek olan binalardır. Bu nedenle üniversite binaları enerji tasarrufunu sağlamak ve karbon salımını azaltmada önemli bir rol oynar. Son yıllarda öğrenci ve üniversite binası sayısında önemli artışlar görülür. Bu artış, mekânların etkin kullanımının önemini arttırmaktadır. Çoğunlukla üniversite binaları mekân yerleşimleri ve doluluk oranlarına dikkat edilmeden ısıtma ve soğutması yapılmaktadır. Farklı yönlere bakan sınıflardaki optimum doluluk oranları ile bina enerji verimliliğini iyileştirmeyi amaçlayan bu çalışmada, hesaplamalı simülasyon ve çok amaçlı optimizasyonu bütünleştiren RSM yöntemi kullanılmıştır. Bu çalışmada, bina simülasyonu için öncelikle bina doluluk oranları hesaplanmış ve hesaplamalar doğrultusunda çok amaçlı optimizasyon için tasarım çözümleri üretilmiştir. Farklı dönemlerde enerji tüketimlerine ait arzu edilebilirliğin yüksek olduğu optimum çözümlere ait doluluk oranları aynı döneme ait sabah ve öğleden sonraki periyotlarda farklılık göstermesi en dikkat çekici bulgulardandır. Optimum çözümler ile ısıtma tüketiminde yaklaşık %2, soğutma tüketiminde ise %9,3 oranında enerji tasarrufu sağlanmıştır. Ayrıca ısıtma amaçlı enerji tüketimi için en etkili parametre kuzey yönlü doluluk oranı iken soğutma amaçlı enerji tüketimi için en etkili parametrenin batı yönlü doluluk oranı olmuştur. Doğu yönlü doluluk oranının ise en az etkili parametre olduğu bulunmuştur. Bu çalışma, eğitim binalarında enerji tasarrufu için mekân kullanımı ve yerleşimi konusunda yapılacak gelecekteki araştırmalara yeni bir bakış sunmayı hedeflemektedir.

Anahtar Kelimeler

Mekân doluluğu, çok amaçlı optimizasyon, yanıt yüzeyi yöntemi, enerji verimliliği, eğitim binalarında mekân kullanımı

Kaynakça

• 1. EnerData. World Energy Climate Statistics-Year Book 2023. https://yearbook.enerdata.net/total-energy/world consumption-statistics.html. Erişim tarihi Şubat 4, 2024.
• 2. United Nations Environment Programme, 2022 Global Status Report for Buildings and Construction: Towards a Zero emission, Efficient and Resilient Buildings and Construction Sector, 2022.
• 3. Yılmaz Y., Koçlar Oral G., An approach for cost and energy efficient retrofitting of a lower secondary school building, Journal of the Faculty of Engineering and Architecture of Gazi University, 34 (1), 393-407, 2019.
• 4. Gul M. S., Patidar S., Understanding the energy consumption and occupancy of a multi-purpose academic building, Energy Build, 87, 155–165, 2015.
• 5. Sun Y., Luo X., Liu X., Optimization of a university timetable considering building energy efficiency: An approach based on the building controls virtual test bed platform using a genetic algorithm, Journal of Building Engineering, 35, 2021.
• 6. Liu Q., Ren J., Research on the building energy efficiency design strategy of Chinese universities based on green performance analysis, Energy Build, 224.
• 7. Chung M. H., Rhee E. K., Potential opportunities for energy conservation in existing buildings on university campus: A field survey in Korea, Energy Build, 78, 176–182, 2014.
• 8. Robinson O., Kemp S., Williams I., Carbon management at universities: A reality check, in Journal of Cleaner Production, 109–118, 2015.
• 9. Wright T. S. A., Definitions and frameworks for environmental sustainability in higher education, Higher Education Policy, 15 (2), 105–120, 2002.
• 10. Cortese A. D., The Critical Role of Higher Education in Creating a Sustainable Future, Planning for higher education, 31 (3), 15-22, 2003.
• 11. Klein-Banai C., Theis T. L., Quantitative analysis of factors affecting greenhouse gas emissions at institutions of higher education, in Journal of Cleaner Production, 48, 29–38, 2013.
• 12. Yeo J., Wang Y., An A. K., Zhang L., Estimation of energy efficiency for educational buildings in Hong Kong, J Clean Prod, 235, 453–460, 2019.
• 13. Lindberg T., Kaasalainen T., Moisio M., Mäkinen A., Hedman M., Vinha J., Potential of space zoning for energy efficiency through utilization efficiency, Advances in Building Energy Research, 14 (1), 19–40, 2020.
• 14. Junnila S., The Environmental Impact of an Office Building Throughout its Life Cycle, 2004.
• 15. Sartori I., Hestnes A. G., Energy use in the life cycle of conventional and low-energy buildings: A review article, Energy Build, 39 (3), 249–257, 2007.
• 16. Junnila S., Horvath A., Asce A. M., Life-Cycle Environmental Effects of an Office Building, Journal of Infrastructure Systems, 9 (4), 2003.
• 17. Thewes A., Maas S., Scholzen F., Waldmann D., Zürbes A., Field study on the energy consumption of school buildings in Luxembourg, Energy Build, 68, 460–470, 2014.
• 18. Airaksinen M., Energy use in day care centers and schools, Energies, 4 (6), 998–1009, 2011.
• 19. Güğül G.N., Köksal M.A., Economic evaluation of the methods used to reduce energy consumption of a single detached house, Journal of the Faculty of Engineering and Architecture of Gazi University, 34 (1), 215–234, 2019.
• 20. Azar E., Menassa C. C., A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings, Energy Build, 55, 841–853, 2012.
• 21. Huovila A., Tuominen P., Airaksinen M., Effects of building occupancy on indicators of energy efficiency, Energies, 10 (5), 2017.
• 22. Rozendaal E., Improving Building Energy Efficiency by Optimizing Occupancy Patterns Using Office Hoteling,” Eindhoven University of Technology, Eindhoven, 2019.
• 23. Gu Y., Lo A., Niemegeers I., A survey of indoor positioning systems for wireless personal networks, IEEE Communications Surveys and Tutorials, 11 (1), 13–32, 2009.
• 24. Meyn S., Surana A., Lin Y., Oggianu S. M., Narayanan S., Frewen T. A., A sensor-utility-network method for estimation of occupancy in buildings’, in Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shangai, 1494–1500, 2009.
• 25. Yang J., Santamouris M., Lee S. E., Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings, Energy Build, 121, 344–349, 2016.
• 26. Wang W., Chen J., Wei W., Jiayu C., Demand-driven hvac control in large space based on occupancy distribution detection through ındoor positioning systems, CLIMA 2016-proceedings of the 12th REHVA World Congress, 2016.
• 27. Zhao J., Lasternas B., Lam K. P., Yun R., Loftness V., Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining, Energy Build,.82, 341–355, 2014.
• 28. Yang Z., Becerik-Gerber B., The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use, Energy Build, 78, 113–122, 2014.
• 29. Dong B., Lam K. P., Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network, J Build Perform Simul, 4 (4), 359–369, 2011.
• 30. Motta Cabrera D. F., Zareipour H., Data association mining for identifying lighting energy waste patterns in educational institutes, Energy Build, 62, 210–216, 2013.
• 31. Song K., Kim S., Park M., Lee H. S., Energy efficiency-based course timetabling for university buildings, Energy, 139, 394–405, 2017.
• 32. Cacchiani V., Caprara A., Roberti R., Toth P., A new lower bound for curriculum-based course timetabling, Comput Oper Res, 40 (10), 2466–2477, 2013.
• 33. Bettoni L., Zavanella L., Potential Energy Benchmark for Lecture Timetable Problem, TECNICA ITALIANA-Italian Journal of Engineering Science, 63, 173–180, 2019.
• 34. Jafarinejad T., Erfani A., Fathi A., Shafii M. B., Bi-level energy-efficient occupancy profile optimization integrated with demand-driven control strategy: University building energy saving, Sustain Cities Soc, 48, 2019.
• 35. Sethanan K., Theerakulpisut S., Benjapiyaporn C., Improving energy efficiency by classroom scheduling: A case study in a Thai university, in Advanced Materials Research, 1089–1095, 2014.
• 36. Gui X., Gou Z., Zhang F., The relationship between energy use and space use of higher educational buildings in subtropical Australia, Energy Build, 211, 2020.
• 37. Gui X., Gou Z., Lu Y., Reducing university energy use beyond energy retrofitting: The academic calendar impacts, Energy Build, 231, 2021.
• 38. García-Cuadrado J., Conserva A., Aranda J., Zambrana-Vasquez D., García-Armingol T., Millán G., Response Surface Method to Calculate Energy Savings Associated with Thermal Comfort Improvement in Buildings, Sustainability, 14 (5), 2022.
• 39. Baghoolizadeh M., Rostamzadeh-Renani R., Rostamzadeh-Renani M., Toghraie D., A multi-objective optimization of a building’s total heating and cooling loads and total costs in various climatic situations using response surface methodology, Energy Reports, 77520–7538, 2021.
• 40. Sümer-Haydaraslan K., Dikmen N., Investigation of the effects of curtain wall angle on energy consumption in buildings, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (1), 315–325, 2024.
• 41. Pisello A. L., Petrozzi A., Castaldo V. L., Cotana F., On an innovative integrated technique for energy refurbishment of historical buildings: Thermal-energy, economic and environmental analysis of a case study, Appl Energy, 162, 1313–1322, 2014.
• 42. Coakley D., Raftery P., Keane M., A review of methods to match building energy simulation models to measured data, Renewable and Sustainable Energy Reviews, 37, 123–141, 2014.
• 43. Mustafaraj G., Marini D., Costa A., Keane M., Model calibration for building energy efficiency simulation, Appl Energy, 130, 72–85, 2014.
• 44. Huang H., Binti Wan Mohd Nazi W. I., Yu Y., Wang Y., Energy performance of a high-rise residential building retrofitted to passive building standard – A case study, Appl Therm Eng, 181, 2020.
• 45. Kleijnen J. P. C., Response surface methodology for constrained simulation optimization: An overview, Simul Model Pract Theory, 16 (1), 50–64, 2008.
• 46. Ghorbani F., Younesi H., Ghasempouri S. M., Zinatizadeh A. A., Amini M., Daneshi A., Application of response surface methodology for optimization of cadmium biosorption in an aqueous solution by Saccharomyces cerevisiae, Chemical Engineering Journal, 145 (2), 267–275, 2008.
• 47. Khuri A. I., Mukhopadhyay S., Response surface methodology’, Wiley Interdisciplinary Reviews: Computational Statistics, 2 (2), 128–149, 2010.
• 48. Bilen M., Ateş Ç., Bayraktar B., Determination of optimal conditions in boron factory wastewater chemical treatment process via response surface methodolgy, Journal of the Faculty of Engineering and Architecture of Gazi University, 33 (1), 267–278, 2018.
• 49. Liu Y., Jia Wang X., Zhou S., Chen H., Enhancing public building energy efficiency using the response surface method: An optimal design approach, Environ Impact Assess Rev, 87, 2021.
• 50. Witek-Krowiak A., Chojnacka K., Podstawczyk D., Dawiec A., Bubala K., Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process, Bioresour Technol, 160, 150–160, 2014.
• 51. Costa N. R., Lourenço J., Pereira Z. L., Desirability function approach: A review and performance evaluation in adverse conditions, Chemometrics and Intelligent Laboratory Systems, 107 (2), 234–244, 2011.
• 52. Chang K. H., Multiobjective Optimization and Advanced Topics, in e-Design, 1105–1173, 2015.
• 53. Lee D. H., Jeong I. J., Kim K. J., A desirability function method for optimizing mean and variability of multiple responses using a posterior preference articulation approach, Qual Reliab Eng Int, 34 (3), 360–376, 2018.
• 54. Bezerra M. A., Santelli R. E., Oliveira E. P., Villar L. S., Escaleira L. A., Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta, 76 (5), 965–977, 2008.
• 55. Myers R. H., Montgomery D. C., Anderson-Cook C. M., Response surface methodology, Process and product optimization using designed experiments, WILEY, 2016.
• 56. Li Q., Zhang L., Zhang L., Wu X., Optimizing energy efficiency and thermal comfort in building green retrofit, Energy, 237, 2021.
• 57. Kadrić D., Aganović A., Kadrić E., Multi-objective optimization of energy-efficient retrofitting strategies for single-family residential homes: Minimizing energy consumption, CO2 emissions and retrofit costs, Energy Reports, 10, 1968–19812023.
• 58. Kim D. D., Suh H. S., Heating and cooling energy consumption prediction model for high-rise apartment buildings considering design parameters, Energy for Sustainable Development, 61, 1–14, 2021.