Deep convolutional learning for general early design stage prediction models
Tài liệu tham khảo
Zapata-Lancaster, 2016, Tools for low-energy building design: an exploratory study of the design process in action, Archit. Eng. Des. Manag., 12, 279
Bleil de Souza, 2012, Contrasting paradigms of design thinking: the building thermal simulation tool user vs. the building designer, Autom. Constr., 22, 112, 10.1016/j.autcon.2011.09.008
Attia, 2012, Simulation-based decision support tool for early stages of zero-energy building design, Energy Build., 49, 2, 10.1016/j.enbuild.2012.01.028
Shiel, 2018, Parametric analysis of design stage building energy performance simulation models, Energy Build., 172, 78, 10.1016/j.enbuild.2018.04.045
Hamedani, 2015, Evaluation of performance modelling: optimizing simulation tools to stages of architectural design, Procedia Eng., 118, 774, 10.1016/j.proeng.2015.08.513
Van Gelder, 2014, Comparative study of metamodelling techniques in building energy simulation: guidelines for practitioners, Simul. Model. Pract. Theory, 49, 245, 10.1016/j.simpat.2014.10.004
Geyer, 2018, Component-based machine learning for performance prediction in building design, Appl. Energy, 228, 1439, 10.1016/j.apenergy.2018.07.011
Singaravel, 2018, Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction, Adv. Eng. Informatics, 38, 81, 10.1016/j.aei.2018.06.004
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Magalhães, 2017, Modelling the relationship between heating energy use and indoor temperatures in residential buildings through Artificial Neural Networks considering occupant behavior, Energy Build., 151, 332, 10.1016/j.enbuild.2017.06.076
Ascione, 2017, CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building, Energy Build., 146, 200, 10.1016/j.enbuild.2017.04.069
Yang, 2005, On-line building energy prediction using adaptive artificial neural networks, Energy Build., 37, 1250, 10.1016/j.enbuild.2005.02.005
Ekici, 2009, Prediction of building energy consumption by using artificial neural networks, Adv. Eng. Softw., 40, 356, 10.1016/j.advengsoft.2008.05.003
Kusiak, 2012, Modeling and optimization of HVAC systems using a dynamic neural network, Energy, 42, 241, 10.1016/j.energy.2012.03.063
Hou, 2006, Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique, Appl. Energy, 83, 1033, 10.1016/j.apenergy.2005.08.006
Chari, 2017, Building energy performance prediction using neural networks, Energy Efficiency, 1
Yao, 2010, Prediction of building energy consumption at early design stage based on artificial neural network, Adv. Mater. Res., 108, 580, 10.4028/www.scientific.net/AMR.108-111.580
Lazrak, 2016, Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation, Renew. Energy, 86, 1009, 10.1016/j.renene.2015.09.023
Neto, 2008, Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption, Energy Build., 40, 2169, 10.1016/j.enbuild.2008.06.013
Paudel, 2017, A relevant data selection method for energy consumption prediction of low energy building based on support vector machine, Energy Build., 138, 240, 10.1016/j.enbuild.2016.11.009
Zhang, 2016, Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique, Energy Build., 126, 94, 10.1016/j.enbuild.2016.05.028
Dong, 2005, Applying support vector machines to predict building energy consumption in tropical region, Energy Build., 37, 545, 10.1016/j.enbuild.2004.09.009
Li, 2009, Applying support vector machine to predict hourly cooling load in the building, Appl. Energy, 86, 2249, 10.1016/j.apenergy.2008.11.035
Zhao, 2012, Feature selection for predicting building energy consumption based on statistical learning method, J. Algorithm. Comput. Technol., 6, 59, 10.1260/1748-3018.6.1.59
Tso, 2007, Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks, Energy, 32, 1761, 10.1016/j.energy.2006.11.010
Zhang, 2018, On the feature engineering of building energy data mining, Sustain. Cities Soc., 39, 508, 10.1016/j.scs.2018.02.016
Catalina, 2008, Development and validation of regression models to predict monthly heating demand for residential buildings, Energy Build., 40, 1825, 10.1016/j.enbuild.2008.04.001
Jaffal, 2017, A metamodel for building energy performance, Energy Build., 151, 501, 10.1016/j.enbuild.2017.06.072
Amasyali, 2018, A review of data-driven building energy consumption prediction studies, Renew. Sustain. Energy Rev., 81, 1192, 10.1016/j.rser.2017.04.095
Fan, 2017, A short-term building cooling load prediction method using deep learning algorithms, Appl. Energy, 195, 222, 10.1016/j.apenergy.2017.03.064
Marino, 2016, Building energy load forecasting using Deep Neural Networks, 7046
Li, 2017, Building energy consumption prediction: an extreme deep learning approach, Energies, 10, 1525, 10.3390/en10101525
Mocanu, 2016, Deep learning for estimating building energy consumption, Sustain. Energy, Grids Networks, 6, 91, 10.1016/j.segan.2016.02.005
Zhong, 2019, Convolutional neural network: deep learning-based classification of building quality problems, Adv. Eng. Informat., 40, 46, 10.1016/j.aei.2019.02.009
Lu, 2017, Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification, Adv. Eng. Informat., 32, 139, 10.1016/j.aei.2017.02.005
Fang, 2019, A deep learning-based approach for mitigating falls from height with computer vision: convolutional neural network, Adv. Eng. Informatics, 39, 170, 10.1016/j.aei.2018.12.005
Fang, 2018, Automated detection of workers and heavy equipment on construction sites: a convolutional neural network approach, Adv. Eng. Informatics, 37, 139, 10.1016/j.aei.2018.05.003
B. Doshi-Velez, Finale, Kim, Towards a rigorous science of interpretable machine learning, arXiv Prepr., 2017.
Singaravel, 2017, Component-based machine learning modelling approach for design stage building energy prediction: weather conditions and size, 2617
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. 2016.
Achille, 2018, Emergence of invariance and disentanglement in deep representations, vol. 18, 1
Scherer, 2010, Evaluation of pooling operations in convolutional architectures for object recognition, vol. 6354, no. PART 3, 92
Oh, 2018, Learning to exploit invariances in clinical time-series data using sequence transformer networks, Proc. Mach. Learn. Res., 85, 1
Hopfe, 2011, Uncertainty analysis in building performance simulation for design support, Energy Build., 43, 2798, 10.1016/j.enbuild.2011.06.034
Østergård, 2016, Building simulations supporting decision making in early design – a review, Renew. Sustain. Energy Rev., 61, 187, 10.1016/j.rser.2016.03.045
A. Paszke et al., Automatic differentiation in pytorch, 2017.