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id ecaade2017_269
authors Rahmani Asl, Mohammad, Das, Subhajit, Tsai, Barry, Molloy, Ian and Hauck, Anthony
year 2017
title Energy Model Machine (EMM) - Instant Building Energy Prediction using Machine Learning
source Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 277-286
summary In the process of building design, energy performance is often simulated using physical principles of thermodynamics and energy behaviour using elaborate simulation tools. However, energy simulation is computationally expensive and time consuming process. These drawbacks limit opportunities for design space exploration and prevent interactive design which results in environmentally inefficient buildings. In this paper we propose Energy Model Machine (EMM) as a general and flexible approximation model for instant energy performance prediction using machine learning (ML) algorithms to facilitate design space exploration in building design process. EMM can easily be added to design tools and provide instant feedback for real-time design iterations. To demonstrate its applicability, EMM is used to estimate energy performance of a medium size office building during the design space exploration in widely used parametrically design tool as a case study. The results of this study support the feasibility of using machine learning approaches to estimate energy performance for design exploration and optimization workflows to achieve high performance buildings.
keywords Machine Learning; Artificial Neural Networks; Boosted Decision Tree; Building Energy Performance; Parametric Modeling and Design; Building Performance Optimization
series eCAADe
email mohammad.asl@autodesk.com
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100%; open Ansari, F, Mokhtar, A, Abbas, K and Adam, N (2005) Find in CUMINCAD A simple approach for building cooling load estimation , Am. J. Environ. Sci, 1(3), pp. 209-212

100%; open Asl, MR, Zarrinmehr, S, Bergin, M and Yan, W (2015) Find in CUMINCAD BPOpt: A framework for BIM-based performance optimization , Energy and Buildings, 108, pp. 401-412

100%; open Bauer, M and Scartezzini, JL (1998) Find in CUMINCAD A simplified correlation method accounting for heating and cooling loads in energy-efficient buildings , Energy and Buildings, 27(2), pp. 147-154

100%; open Catalina, T, Virgone, J and Blanco, E (2008) Find in CUMINCAD Development and validation of regression models to predict monthly heating demand for residential buildings , Energy and buildings, 40(10), pp. 1825-1832

100%; open DOE, US (2012) Find in CUMINCAD Building energy software tools directory , Department of Energy

100%; open Dong, B, Cao, C and Lee, SE (2005) Find in CUMINCAD Applying support vector machines to predict building energy consumption in tropical region , Energy and Buildings, 37(5), pp. 545-553

100%; open Dounis, AI (2010) Find in CUMINCAD Artificial intelligence for energy conservation in buildings , Advances in Building Energy Research, 4(1), pp. 267-299

100%; open Guo, X, Li, W and Iorio, F (2016) Find in CUMINCAD Convolutional neural networks for steady flow approximation , Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 481-490

100%; open Hopfield, JJ (1982) Find in CUMINCAD Neural networks and physical systems with emergent collective computational abilities , Proceedings of the national academy of sciences, 79(8), pp. 2554-2558

100%; open Hunter, JD (2007) Find in CUMINCAD Matplotlib: A 2D graphics environment , Computing In Science & Engineering, 9(3), pp. 90-95

100%; open Krarti, M (2003) Find in CUMINCAD An overview of artificial intelligence-based methods for building energy systems , TRANSACTIONS-AMERICAN SOCIETY OF MECHANICAL ENGINEERS JOURNAL OF SOLAR ENERGY ENGINEERING, 125(3), pp. 331-342

100%; open Li, Q, Meng, Q, Cai, J, Yoshino, H and Mochida, A (2009) Find in CUMINCAD Applying support vector machine to predict hourly cooling load in the building , Applied Energy, 86(10), pp. 2249-2256

100%; open Pedregosa, F, Varoquaux, G, Gramfort, A, Michel, V, Thirion, B, Grisel, O, Blondel, M, Prettenhofer, P, Weiss, R and Dubourg, V (2011) Find in CUMINCAD Scikit-learn: Machine learning in Python , Journal of Machine Learning Research, 12(Oct), pp. 2825-2830

100%; open Pour, ZA and Ayat, S (2014) Find in CUMINCAD Comparison between artificial neural network learning algorithms for prediction of student average considering effective factors in Learning and educational progress , Journal of Mathematics and Computer Science, 8, pp. 215-225

100%; open Roe, BP, Yang, HJ, Zhu, J, Liu, Y, Stancu, I and McGregor, G (2005) Find in CUMINCAD Boosted decision trees as an alternative to artificial neural networks for particle identification , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 543(2), pp. 577-584

100%; open Tsanas, A and Xifara, A (2012) Find in CUMINCAD Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , Energy and Buildings, 49, pp. 560-567

100%; open Walt, Svd, Colbert, SC and Varoquaux, G (2011) Find in CUMINCAD The NumPy array: a structure for efficient numerical computation , Computing in Science & Engineering, 13(2), pp. 22-30

100%; open Yu, Z, Haghighat, F, Fung, BC and Yoshino, H (2010) Find in CUMINCAD A decision tree method for building energy demand modeling , Energy and Buildings, 42(10), pp. 1637-1646

100%; open Zhang, J and Haghighat, F (2010) Find in CUMINCAD Development of Artificial Neural Network based heat convection algorithm for thermal simulation of large rectangular cross-sectional area Earth-to-Air Heat Exchangers , Energy and Buildings, 42(4), pp. 435-440

100%; open Zhao, Hx and Magoul?s, F (2012) Find in CUMINCAD A review on the prediction of building energy consumption , Renewable and Sustainable Energy Reviews, 16(6), pp. 3586-3592

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