کاربرد شبکه های عصبی مصنوعی در تخمین مصرف انرژی فضاهای آموزشی
محورهای موضوعی : معماری
1 - استادیار گروه معماری، دانشکده معماری و شهرسازی، دانشگاه هنر، تهران، ایران
کلید واژه: پنجره کلاس درس, مصرف انرژی, DOE-2, هوش مصنوعی, شبکه عصبی مصنوعی,
چکیده مقاله :
تاکنون توصی ههای دقیقی برای مهندسان معمار جهت تعیین ابعاد مناسب پنجره با رویکرد کاهش مصرف انرژی برای فضاهایآموزشی ارائه نشده است. برای آنکه طراحان فضاهای آموزشی ب هدوراز محاسبات هزین هبر و وق تگیرِ شبیه سازی انرژی قادر بهتعیین سطح مناسب پنجره و یا حداقل اولویت بندی گزین ههای ممکن نورگیری باشند، در تحقیق حاضر بر پایه هوش مصنوعیساختاری جدید ارائه شده است که م یتواند هزینه انرژی را در مدت بهر هبرداری از یک کلاس درس استاندارد، به عنوان مه مترینبخش فضای آموزشی، پی شبینی نماید. بدین منظور، 288 سناریوی نورگیری شبیه سازی شده و نتایج حاصله برای آموزش شبک هعصبی مصنوعی استفاد هشد ه است. آزمو نهای شبکه آموزش نشان م یدهد که ساختار پیشنهادی ب هخوبی م یتواند جایگزین مدلشبیه ساز مصرف انرژی گردد و طراح تنها با مشخص نمودن جهت نورگیری و نسبت سطح پنجره به سطح دیوار کلاس م یتواندهزینه مصرف گاز و الکتریسیته را در مدت بهر هبرداری با دقت بسیار خوبی پیش بینی نماید.
Size of classroom’s windows has significant effects on both comfort level of users and electricity consumption forlighting. Moreover, windows are the main source of energy loss in classrooms in both cooling and heating sectors.Considering the large number of educationalbuildings and long life cycle of such them, choosing proper window size is crucial for energy saving in sustainablearchitectural design. Despite the role that windows have in energy consumption, the literatures are surprisinglylimited in providing detailed recommendations for architects in determining the appropriate window size in differentclimates. Therefore, energy based window design has always been complicated for architects due to the numberof involved different components and variables. In order to help the architectural designers, in this paper a newmethodology is developed using a well-known artificial intelligence technique. In the proposed methodology, apredictive model for energy consumption cost in terms of window to wall ratio (WWR) and the window facing wascreated using Artificial Neural Network (ANN). The methodology consisted of a limited sets of direct numericalenergy simulations for any specific climatic zone to generate the data required for training the ANN. The DOE-2issuggested in the proposed methodology for direct numerical energy simulations of the daylighting scenarios requiredfor training the ANN. The DOE-2 is a popular and powerful computational model developed with financial supportof U.S. department of energy. The trained ANN-based model provides a fast and convenient way of comparing thedifferent daylighting scenarios in designing stage. Indeed, further calculations for direct energy simulations are notnecessary and an architect can readily utilize the trained ANN-based model as a powerful tool for forecasting thetotal energy consumption cost. In order to show the applicability and performance of the proposed approach, 288daylighting scenarios for a standard classroom in a warm and dry climate, Shiraz-Iran, were simulated to determinethe corresponding electric and gas consumption. A square classroom of side 7.4 m is the standard classroom definedby Iranian Organization for Renovating, Developing and Equipping Schools. The DOE-2 is utilized for simulating thedefined standard classroom in the study area for estimating the annual gas and electric consumption of the generatedscenarios over a 50 years period. Included daylighting scenarios were randomly split into train and test sets. In thisstudy, around 80 percent of data were used for training, and the rest were used to evaluate the performance of thetrained ANN. The best training and learning functions for different number of layers and neurons was determined ina trial-error process. Correlation Coefficient (CC), Mean square error (MSE) and Root mean square error (RMSE)are the statistical indices used for training procedure. The best results were obtained with 2 hidden layers and 6neurons per layer. The 'Levenverg-Marquardt back propagation (trainlm)' and 'perceptron weight and bias learningfunction (learnp)' were the best training functions found for this research. The results show that the trained ANN canaccurately predict the total energy consumption cost (RMSE=0.0811, MSE=0.0066, and CC=0.9672).
1. سازمان نوسازی، توسعه و تجهیز مدارس کشور. (1386). ضوابط و معیارهای طراحی فضاهای آموزشی. (ویرایش 3). تهران: دفتر فنی سازمان نوسازی، توسعه و تجهیز مدارس کشور.
2. عصر ایران. (1389). دقیق‌ترین آمار دانش‌آموزی و نیروی انسانی تاریخ آموزش و پرورش. کد خبر: ۱۵۶۳۹۱. بازیابی ۲۵بهمن ۱۳۸۹، از .www.asriran.com/fa/news/156391
3. اداره کل هواشناسی استان فارس. (1396). میانگین‌های اقلیمی ایستگاه هواشناسی سینوپتیک شیراز، دوره آماری (90-1350). www.farsmet.ir/amar/syngraph/shiraz.pdf.
4. دفتر تدوین مقررات ملی ساختمان. (1395). طرح و اجرای تأسیسات برقی ساختمان‌ها-مبحث سیزدهم مقررات ملی ساختمان ایران. تهران: مرکز تحقیقات راه، مسکن و شهرسازی.
5. کریم پور، علیرضا؛ دیبا، دارب؛ و اعتصام، ایرج. (1396). تحلیل تأثیر آفتاب‌گیرهای داخلی بر مصرف انرژی با استفاده از مدل‌های شبیه‌سازی (مطالعه موردی: واحد مسکونی در تهران). هویت شهر، 11 (30)، 17-30.
6. زارع، فائزه؛ و حیدری، شاهین. (1394). طراحی معماری با بهره‌گیری از روشنایی طبیعی رویکردی در طراحی کتابخانه برای شهر تهران. هویت شهر، 9 (24)، 55-64.
7. زمردیان، زهرا سادات؛ تحصیل دوست، محمد. (1394). اعتبارسنجی نرم‌افزارهای شبیه‌سازی انرژی در ساختمان: با رویکرد تجربی و مقایسه‌ای. نشریه انرژی ایران. 18 (4)، 115-132.
8. Al-Rabghi, O. M., Al-Beirutty, M.H., &Fathalah, K. A. (1999). Estimation and measurement of electric energy consumption due to air conditioning cooling load. Energy Conversion & Management, 40, 1527-1542.
9. Argiriou, A.A., Bellas-Velidis, I.,&Balaras, C.A. (2000). Development of a neural network heating controller for solar buildings.Neural Networks, 13, 811-820.
10. ASHRAE 55 (2010). ANSI/ASHRAE Standard 55-2010, ASHRAE Environmental Conditions for Human Occupancy, Atlanta, Ga, USA: American Society of Heating, Refrigeration and Air Conditioning Engineers, Inc.
11. Bodart, M., De Herde, A. (2002). Global energy savings in offices buildings by use of daylighting. Energy Build, 34, 421–429.
12. Calise, F. (2010). Thermo economic analysis and optimization of high efficiency solar heating and cooling systems for different Italian school buildings and climates.Energy and Buildings,42 (7), 992-1003.
13. Corgnati, S. P., Corrado, V., &Filippi, M. (2008). A method for heating consumption assessment in existing buildings: A field survey concerning 120 Italian schools. Energy and Buildings, 40, 801–809.
14. Deb, C., Eang, L. S., Yang, J., &Santamouris, M. (2016). Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks. Energy and Buildings, 121, 284-297.
15. Demuth,H., & Beale,M. (2002). Neural Network Toolbox User`s Guide. Math Works Inc., Natick, MA, U.S.A.
16. Dombaycı, Ö. A. (2010). The prediction of heating energy consumption in a model house by using artificial neural networks in Denizli–Turkey. Advances in Engineering Software, 41(2), 141-147.
17. Doulos, L., Tsangrassoulis, A., &Topalis, F. (2008). Quantifying energy savings in daylight responsive systems:The role of dimming electronic ballasts. Energy and Buildings, 40, 36–50.
18. Fallahnia, M., Kerachian, R., Etessam, I.,&Majedi, H. (2012). The impact of window characteristics on gas and electric costs in educational buildings: Application of Support Vector Machines. Iranian Journal of Science and Technology. Transactions of Mechanical Engineering, 36 (M2), 193-205.
19. Hong, T., Kim, J.M., &Koo, C.W. (2012). LCC and LCCO2 analysis of green roofs in elementary schools with energy saving measures. Energy and Buildings, 45, 229–239.
20. Hosseini, M., &Akbari, H. (2016). Effect of cool roofs on commercial buildings energy use in cold climates. Energy and Buildings, 114, 143-155.
21. Ihm, P., Nemri, A., &Krarti, M. (2009). Estimation of lighting energy savings from daylighting. Building and Environment, 44, 509– 514.
22. Jovanović, R., Aleksandra, Ž.,Sretenović, A.,&Živković, B. D. (2015). Ensemble of various neural networks for prediction of heating energy consumption.Energy and Buildings, 94, 189-199.
23. Kasperkiewics, J., Racz J.,&Dubrawski A. (1995). HPC strength predictionusing ANN.ASCE. Journal of Comp. Civil Eng, 4, 279–284
24. Kumar, R., Aggarwal, R. K., &Sharma, J. D. (2013). Energy analysis of a building using artificial neural network: A review.Energy and Buildings,65, 352-358.
25. Lam, J. C., Tsang, C. L., &Yang, L. (2006). Impacts of lighting density on heating and cooling loads in different climates in China.Energy Conversion and Management, 47, 1942–1953.
26. Li, X. P., Yin, B., Yang, C. X., &Zhou, H. Z. (2013). Application Potential of Solar-Shading in Tropical Island Cities.Applied Mechanics and Materials, 361, 312-317.
27. Loutzenhiser, P.G., &Maxwell, G.M. (2006).A comparison of DOE-2-2.1E daylighting and HVAC system interactions to actual building performance.ASHRAE Transactions, 112(2), 409-417.
28. Moon, J., W., Jung, S.K., Kim,& J.J. (2009). Application of ANN (Artificial Neural Network) in residential thermal control. IBPSA. Proceeding of Eleventh International IBPSA Conference. July 27-30, (pp.64-71). Glasgow, Scotland.
29. Mottahedi, M., Mohammadpour, A., Amiri, S. S., Riley, D., &Asadi, S. (2015). Multi-linear regression models to predict the annual energy consumption of an office building with different shapes. Procedia Engineering, 118, 622-629.
30. Perez, Y., V., &Capeluto, I., G. (2009). Climatic considerations in school building design in the hot–humid climate for reducing energy consumption.Applied Energy, 86, 340–348.
31. Rumelhart, D.E., Hinton, G.E. and Williams, R.J., 1986. Learning representations by back-propagating errors. nature, 323(6088), p.533.
32. Rumelhart, David E., Hinton, Geoffrey E., Williams & Ronald J. (1986). Learning representations by back-propagating errors.Nature, 323(6088), 533–536.
33. Singh, I., &Michaelowa, A. (2004). HWWA Discussion Paper 289, Hamburg Institute of International Economics, Hamburg, Germany.
34. Ballal, T.M., & Sher, W. D. (2003). Artificial neural network for the selection of buildable structural systems. Engineering, Construction and Architectural Management, 10 (4), 263-271.
35. Tsai, C.P., &Lee, T.L. (1999). Back-Propagation neural network in tidal level forecasting, ASCE, Journal of Waterway, Port, Coastal and Ocean Engineering, 125, 195-202.
36. Zhu, Y. (2006). Applying computer-based simulation to energy auditing: A case study. Energy and Buildings, 38, 421–428.
37. Zhu, L., Hurt, R., Correa, D., &Boehm, R. (2009). Comprehensive energy and economic analyses on a zero energy house versus a conventional house, Energy, 34, 1043–1053.