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).
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