CoolVox: Advanced 3D convolutional neural network models for predicting solar radiation on building facades

[1]  Joseph Andrew Clarke,et al.  A vision for building performance simulation: a position paper prepared on behalf of the IBPSA Board , 2015 .

[2]  Ravinesh C. Deo,et al.  Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms , 2019, Applied Energy.

[3]  Robert B. Miller,et al.  Response time in man-computer conversational transactions , 1899, AFIPS Fall Joint Computing Conference.

[4]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[5]  Soteris A. Kalogirou,et al.  Machine learning methods for solar radiation forecasting: A review , 2017 .

[6]  Yeonsook Heo,et al.  Calibration of building energy models for retrofit analysis under uncertainty , 2012 .

[7]  Xiangyu Wang,et al.  Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer's disease , 2019, Neurocomputing.

[8]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007 .

[9]  André De Herde,et al.  A simple design tool for the thermal study of an office building , 2002 .

[10]  S. Ranji Ranjithan,et al.  Multivariate regression as an energy assessment tool in early building design , 2012 .

[11]  Ralph Evins,et al.  Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones , 2020 .

[12]  Soteris A. Kalogirou,et al.  Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .

[13]  Godfried Augenbroe,et al.  Advanced Building Simulation , 2004 .

[14]  Devin K. Harris,et al.  3D InspectionNet: a deep 3D convolutional neural networks based approach for 3D defect detection on concrete columns , 2019, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[15]  Christoph F. Reinhart,et al.  DIVA 2.0: INTEGRATING DAYLIGHT AND THERMAL SIMULATIONS USING RHINOCEROS 3D, DAYSIM AND ENERGYPLUS , 2011 .

[16]  Elie Azar,et al.  Integrating building performance simulation in agent-based modeling using regression surrogate models: A novel human-in-the-loop energy modeling approach , 2016 .

[17]  Ana Paula Melo,et al.  Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates , 2016 .

[18]  Cyril Voyant,et al.  Forecasting of preprocessed daily solar radiation time series using neural networks , 2010 .

[19]  Shady Attia,et al.  EARLY DESIGN SIMULATION TOOLS FOR NET ZERO ENERGY BUILDINGS: A COMPARISON OF TEN TOOLS , 2011 .

[20]  Christian Inard,et al.  Fast method to predict building heating demand based on the design of experiments , 2009 .

[21]  R. K. Price,et al.  Combining semi-distributed process-based and data-driven models in flow simulation: A case study of the Meuse river basin , 2009 .

[22]  Gerardo Maria Mauro,et al.  Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach , 2017 .

[23]  Vladimir M. Krasnopolsky,et al.  Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction , 2006, Neural Networks.

[24]  Dinggang Shen,et al.  Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation , 2017, Comput. Medical Imaging Graph..

[25]  Christina J. Hopfe,et al.  Robust multi-criteria design optimization in building design , 2012 .

[26]  Amit Kumar Yadav,et al.  Solar radiation prediction using Artificial Neural Network techniques: A review , 2014 .

[27]  Piet Demeester,et al.  A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design , 2010, J. Mach. Learn. Res..

[28]  Aman Jantan,et al.  State-of-the-art in artificial neural network applications: A survey , 2018, Heliyon.

[29]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[30]  Adrian Chong,et al.  Guidelines for the Bayesian calibration of building energy models , 2018, Energy and Buildings.

[31]  Sebastian Scherer,et al.  VoxNet: A 3D Convolutional Neural Network for real-time object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[32]  Lynne E. Parker,et al.  Constructing large scale surrogate models from big data and artificial intelligence , 2017 .

[33]  Yang Liu,et al.  O-CNN , 2017, ACM Trans. Graph..

[34]  Staf Roels,et al.  Comparative study of metamodelling techniques in building energy simulation: Guidelines for practitioners , 2014, Simul. Model. Pract. Theory.

[35]  Holly Wasilowski Samuelson,et al.  Parametric Energy Simulation in Early Design: High-Rise Residential Buildings in Urban Contexts , 2016 .

[36]  Tiberiu Catalina,et al.  Multiple regression model for fast prediction of the heating energy demand , 2013 .

[37]  K. Kaba,et al.  Estimation of daily global solar radiation using deep learning model , 2018, Energy.

[38]  Evan B. Goldstein,et al.  Machine learning components in deterministic models: hybrid synergy in the age of data , 2015, Front. Environ. Sci..

[39]  Jesús Polo,et al.  Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index , 2005 .

[40]  Yacine Rezgui,et al.  Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption , 2017 .

[41]  Mohamed Mohandes,et al.  Estimation of global solar radiation using artificial neural networks , 1998 .

[42]  R. Bird,et al.  Simplified clear sky model for direct and diffuse insolation on horizontal surfaces , 1981 .

[43]  Philippe Rigo,et al.  A review on simulation-based optimization methods applied to building performance analysis , 2014 .

[44]  Ralph Evins,et al.  Surrogate modelling for sustainable building design – A review , 2019, Energy and Buildings.

[45]  Philipp Geyer,et al.  Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction , 2018, Adv. Eng. Informatics.

[46]  Luis Perez,et al.  The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.

[47]  Soteris A. Kalogirou,et al.  Applications of artificial neural networks in energy systems , 1999 .

[48]  Haralambos Sarimveis,et al.  Optimization of window-openings design for thermal comfort in naturally ventilated buildings , 2012 .

[49]  Vladan Babovic,et al.  Neural networks as routine for error updating of numerical models , 2001 .

[50]  Wei Tian,et al.  A review of sensitivity analysis methods in building energy analysis , 2013 .

[51]  Philipp Geyer,et al.  Simulation-based Decision-making in Early Design Stages , 2015 .

[52]  Thomas Brox,et al.  Orientation-boosted Voxel Nets for 3D Object Recognition , 2016, BMVC.

[53]  Ashu Jain,et al.  Development of effective and efficient rainfall‐runoff models using integration of deterministic, real‐coded genetic algorithms and artificial neural network techniques , 2004 .