صفه

صفه

تخمین انرژی سرمایشی و گرمایشی ساختمان: مقایسه‌ای جامع از مدل‌های یادگیری ماشین؛ مورد مطالعاتی: طراحی پارامتریک نمونۀ استاندارد اشری در اقلیم تهران

نوع مقاله : مقاله پژوهشی

نویسنده
دانشجوی دکتری معماری، دانشکدة معماری و شهرسازی، دانشگاه شهید بهشتی، تهران، ایران
چکیده
اهداف و پیشینه: بهبود بهره‌وری انرژی در بخش ساختمان، مخصوصاً در مراحل اولیة طراحی، هدفی اساسی برای کاهش میزان انتشار گازهای گلخانه‌ای و نیز مصرف سوخت فسیلی است که این هدفْ مستلزم ‌‌پیش‌بینی دقیق انرژی برای تصمیم‌گیری بهینه است. در سال‌های اخیر، هوش مصنوعی و روش‌های یادگیری ماشین برای پیش‌بینی مصرف انرژی و عملکرد ساختمان پیشنهاد شده‌اند. سادگی مدل‌سازی ساختمان‌ها و استفاده از داده‌های مصرف انرژی ساختمان ‌‌شبیه‌سازی‌شده باعث بهبود مدل‌های یادگیری ماشین شده است.

مواد و روش‌ها: در این مقاله با طراحی پارامتریک نمونة استاندارد اشری (نمونة 600) با متغیرهای ‌‌پیش‌بینی جهت گیری ساختمان، نسبت پنجره به دیوار، ابعاد ساختمان شامل طول، عرض، ارتفاع و کاربری ساختمان و شبیه‌سازی به کمک نرم‌افزارهای EnergyPlus و jEPlus داده‌سازی می‌شود، سپس با استفاده از چندین رویکرد پرکاربرد یادگیری ماشین در پیش‌بینی و بهبود عملکرد انرژی ساختمان، شامل رویکرد رگرسیونی با کمک رگرسیون خطی و مبتنی بر گاوس، رویکرد ‌‌طبقه‌بندی با کمک الگوریتم رگرسیون لجستیک، -Kنزدیک‌ترین همسایه، درخت تصمیم، جنگل‌های تصادفی، ماشین بردار پشتیبان، رویکرد شبکة عصبی چندلایه، رویکرد خوشه‌بندی با کمک الگوریتم‌های -Kمیانگین، و سلسله‌مراتبی و مبتنی بر چگالی، به ‌‌پیش‌بینی انرژی سرمایشی و گرمایشی ساختمان و ‌‌گروه‌بندی داده‌های حاصل پرداخته می‌شود، در ادامه نتایج با چندین مدل یادگیری ماشین تقویتی شامل AdaBoost، ماشین تقویت گرادیان و مشتقات آن مقایسه می‌گردد، دقت مدل‌های ‌‌پیش‌بینی با معیارهای ارزیابی مختلف سنجیده می‌شود،‌ هایپرپارامتر مدل‌های ‌‌پیش‌بینی ‌‌بهینه‌سازی می‌گردد، به ‌‌اعتبارسنجی متقاطع 10برابری پرداخته می‌شود، و درنهایت به‌کمک یادگیری ماشین تفسیرپذیر، نتایج به بهترین شکل تفسیر می‌گردد.

نتایج و جمع‌بندی: نتایج نشان ‌‌می‌دهد که استفاده از مدل ماشین تقویت‌کننده، دقت پیش‌بینی را در مقایسه با مدل‌های یادگیری انفرادی بهبود می‌بخشد و در میان مدل‌های یادگیری ماشین انفرادی، شبکة عصبی مصنوعی و درخت تصمیم بالاترین دقت ‌‌پیش‌بینی را در مسائل رگرسیونی و ‌‌طبقه‌بندی دارند. تأثیر تنظیم شبکة عصبی مصنوعی در افزایش دقت ‌‌پیش‌بینی مدل بیشتر از سایر مدل‌هاست و در ‌‌پیش‌بینی انرژی سرمایشی روش Label Encoding و در ‌‌پیش‌بینی انرژی گرمایشی روش One Hot Encoding برای پیش‌پردازش داده‌ها توصیه ‌‌می‌شود.
کلیدواژه‌ها

عنوان مقاله English

Estimating Building Cooling and Heating Energy: A Comprehensive Comparison of Machine Learning Models; the Case of ASHRAE Standard Sample Parametric Design in Tehran

نویسنده English

Fateme Akhlaghinezhad
Ph.D. Candidate, Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran
چکیده English

Background and Objectives: It has become an imperative to Improve energy efficiency in the building sector, especially in the early stages of design, in order to reduce greenhouse gas emissions and fossil fuel consumption. This requires accurate energy forecasts to optimise decision makings. In recent years, artificial intelligence and machine learning techniques have been proposed to predict energy consumption and building performance. The simplicity of building modelling and the use of simulated building energy consumption data has improved machine learning models.

Materials and Methods: The present research uses a parametric design of the ASHRAE standard sample (Case 600) with the predicting features of building orientation, window-to-wall ratio (WWR), building dimensions ––including length, width, height and building use – and simulation using EnergyPlus and jEPlus software. Then, using several widely-used machine learning approaches in predicting and improving building energy performance – including regression approach with the help of Linear and Gaussian regression, classification approach with the help of Logistic regression, K-Nearest Neighbour, Decision Tree, Random Forests and Support Vector Machine, the Multilayer Neural Network approach, the clustering approach with the help of K-means, Hierarchical and Density-Based algorithms – the cooling and heating energy of the building is predicted, with the resulting data being classified.
The results are then combined with several boosting machine learning models – including AdaBoost, Gradient Boosting Machine and its derivatives – and a comparison is made about the accuracy of prediction models with different evaluation criteria, followed by an optimisation of the hyperparameter of prediction models, a 10-fold cross-validation, and finally an interpretation of the results with the help of interpretable machine learning in the best possible way (SHapley value).

Results and Conclusion: The results show that the use of the Boosting Machine model improves the prediction accuracy, and that compared to single learning models, and among the single machine learning models, the Artificial Neural Network and the Decision Tree have the highest prediction accuracy for the regression and classification problems of building cooling and heating energy prediction after cross-validation. The effect of adjusting the Artificial Neural Network in increasing the accuracy of the model prediction is more than other models, and the Label Encoding method is recommended for predicting the cooling energy and the One Hot Encoding method for predicting the heating energy for data preprocessing.

کلیدواژه‌ها English

Boosting machine learning
Interpretable machine learning
Cross validation
Tuning of hyperparameters
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