Abstract
Data-driven models have become increasingly prominent in the building, architecture, and construction industries. One area ideally suited to exploit this powerful new technology is building performance simulation. Physics-based models have traditionally been used to estimate the energy flow, air movement, and heat balance of buildings. However, physics-based models require many assumptions, significant computational power, and a considerable amount of time to output predictions. Artificial neural networks (ANNs) with prefabricated or simulated data are likely to be a more feasible option for environmental analysis conducted by designers during the early design phase. Because ANNs require fewer inputs and shorter computation times and offer superior performance and potential for data augmentation, they have received increased attention for predicting the surface solar radiation on buildings. Furthermore, ANNs can provide innovative and quick design solutions, enabling designers to receive instantaneous feedback on the effects of a proposed change to a building’s design. This research introduces deep learning methods as a means of simulating the annual radiation intensities and exposure level of buildings without the need for physics-based engines. We propose the CoolVox model to demonstrate the feasibility of using 3D convolutional neural networks to predict the surface radiation on building facades. The CoolVox model accurately predicted the radiation intensities of building facades under different boundary conditions and performed better than ARINet (with average mean square errors of 0.01 and 0.036, respectively) in predicting the radiation intensity both with (validation error = 0.0165) and without (validation error = 0.0066) the presence of boundary buildings.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abiodun OI, Jantan A, Omolara AE, et al. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4: e00938.
Ahmad MW, Mourshed M, Rezgui Y (2017). Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, 147: 77–89.
Ascione F, Bianco N, De Stasio C, et al. (2017). Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy, 118: 999–1017.
Attia SG, Herde AD (2011). Early design simulation tools for net zero energy buildings: A comparison of ten tools. In: Proceedings of the 12th International IBPSA Building Simulation Conference, Sydney, Australia.
Babovic V, Caňizares R, Jensen HR, et al. (2001). Neural networks as routine for error updating of numerical models. Journal of Hydraulic Engineering, 127: 181–193.
Bird RE, Hulstrom RL (1981). Simplified clear sky model for direct and diffuse insolation on horizontal surfaces. Technical Report. Golden, CO, USA: Solar Energy Research Institute.
Catalina T, Iordache V, Caracaleanu B (2013). Multiple regression model for fast prediction of the heating energy demand. Energy and Buildings, 57: 302–312.
Chong A, Menberg K (2018). Guidelines for the Bayesian calibration of building energy models. Energy and Buildings, 174: 527–547.
Clarke J (2015). A vision for building performance simulation: A position paper prepared on behalf of the IBPSA Board. Journal of Building Performance Simulation, 8: 39–43.
Corzo GA, Solomatine DP, de Wit M, et al. (2009). Combining semidistributed process-based and data-driven models in flow simulation: A case study of the Meuse river basin. Hydrology and Earth System Sciences, 13: 1619–1634.
Dey S, Pratiher S, Banerjee S, et al. (2017). SolarisNet: A deep regression network for solar radiation prediction. arXiv preprint arXiv: 1711.08413.
Dizaji MS, Harris DK (2019). 3D InspectionNet: A deep 3D convolutional neural networks based approach for 3D defect detection on concrete columns. In: Proceedings of Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII, Denver, CO, USA.
Dolz J, Desrosiers C, Wang L, et al. (2020). Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation. Computerized Medical Imaging and Graphics, 79: 101660.
Edwards RE, New J, Parker LE, et al. (2017). Constructing large scale surrogate models from big data and artificial intelligence. Applied Energy, 202: 685–699.
Forrester A, Sóbester A, Keane A (2008). Engineering Design via Surrogate Modelling: A Practical Guide. Chichester, UK: John Wiley & Sons.
Ghimire S, Deo RC, Raj N, et al. (2019). Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. Applied Energy, 253: 113541.
Goldstein EB, Coco G (2015). Machine learning components in deterministic models: Hybrid synergy in the age of data. Frontiers in Environmental Science, 3: 33.
Goodfellow I, Bengio Y, Courville A, et al. (2016). Deep Learning. Cambridge, MA, USA: MIT Press.
Gorissen D, Couckuyt I, Demeester P, et al. (2010). A surrogate modeling and adaptive sampling toolbox for computer based design. Journal of Machine Learning Research, 11: 2051–2055.
Gratia E, De Herde A (2002). A simple design tool for the thermal study of an office building. Energy and Buildings, 34: 279–289.
Han J, Chang CK, Malkawi A (2020). AriNet: Using 3D convolutional neural networks to estimate annual radiation intensities on building facades. In: Proceedings of 2020 Building Performance Analysis Simbuild Virtual Conference, Chicago, IL, USA.
Heo Y, Choudhary R, Augenbroe GA (2012). Calibration of building energy models for retrofit analysis under uncertainty. Energy and Buildings, 47: 550–560.
Hopfe CJ, Emmerich M, Marijt R, et al. (2012). Robust multi-criteria design optimization in building design. In: Proceedings of IBPSA Conference Building Simulation and Optimization, Loughborough, UK.
Hygh JS, DeCarolis JF, Hill DB, et al. (2012). Multivariate regression as an energy assessment tool in early building design. Building and Environment, 57: 165–175.
Jaffal I, Inard C, Ghiaus C (2009). Fast method to predict building heating demand based on the design of experiments. Energy and Buildings, 41: 669–677.
Jain A, Srinivasulu S (2004). Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques. Water Resources Research, 40: W04302.
Jakubiec JA, Reinhart CF (2011). DIVA 2.0: Integrating daylight and thermal simulations using Rhinoceros 3D, Daysim and EnergyPlus. In: Proceedings of the 12th International IBPSA Building Simulation Conference, Sydney, Australia.
Kaba K, Sarıgül M, Avcı M, et al. (2018). Estimation of daily global solar radiation using deep learning model. Energy, 162: 126–135.
Kalogirou SA (1999). Applications of artificial neural networks in energy systems: A review. Energy Conversion and Management, 40: 1073–1087.
Kalogirou S, Bojic M (2000). Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy, 25: 479–491.
Krasnopolsky VM, Fox-Rabinovitz MS (2006). Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction. Neural Networks, 19: 122–134.
Malkawi A, Augenbroe G (2004). Advanced Building Simulation. New York, NY: Spon Press.
Maturana D, Scherer S (2015). VoxNet: A 3D Convolutional Neural Network for real-time object recognition. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.
Miller RB (1968). Response time in man-computer conversational transactions. In: Proceedings of Fall Joint Computer Conference, Part I, San Francisco, CA, USA.
Mohandes M, Rehman S, Halawani TO (1998). Estimation of global solar radiation using artificial neural networks. Renewable Energy, 14: 179–184.
Nguyen A-T, Reiter S, Rigo P (2014). A review on simulation-based optimization methods applied to building performance analysis. Applied Energy, 113: 1043–1058.
Paoli C, Voyant C, Muselli M, et al. (2010). Forecasting of pre-processed daily solar radiation time series using neural networks. Solar Energy, 84: 2146–2160.
Papadopoulos S, Azar E (2016). Integrating building performance simulation in agent-based modeling using regression surrogate models: A novel human-in-the-loop energy modeling approach. Energy and Buildings, 128: 214–223.
Perez L, Wang J (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv: 1712.04621.
Rackes A, Melo AP, Lamberts R (2016). Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates. Applied Energy, 174: 256–274.
Ritter F, Geyer P, Borrmann A (2015). Simulation-based decision-making in early design stages. In: Proceedings of the 32nd CIB W78 Conference, Eindhoven, The Netherlands.
Samuelson H, Claussnitzer S, Goyal A, et al. (2016). Parametric energy simulation in early design: High-rise residential buildings in urban contexts. Building and Environment, 101: 19–31.
Sedaghat N, Zolfaghari M, Amiri E, et al. (2016). Orientation-boosted voxel nets for 3D object recognition. arXiv preprint arXiv: 1604.03351.
Singaravel S, Suykens J, Geyer P (2018). Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction. Advanced Engineering Informatics, 38: 81–90.
Stavrakakis GM, Zervas PL, Sarimveis H, et al. (2012). Optimization of window-openings design for thermal comfort in naturally ventilated buildings. Applied Mathematical Modelling, 36: 193–211.
Tian W (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews, 20: 411–419.
Van Gelder L, Das P, Janssen H, et al. (2014). Comparative study of metamodelling techniques in building energy simulation: Guidelines for practitioners. Simulation Modelling Practice and Theory, 49: 245–257.
Voyant C, Notton G, Kalogirou S, et al. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105: 569–582.
Wang GG, Shan S (2007). Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design, 129: 370–380.
Wang P-S, Liu Y, Guo Y-X, et al. (2017). O-CNN: Octree-based convolutional neural networks for 3D shape. ACM Transactions on Graphics, 36(4): 72.
Wang H, Shen Y, Wang S, et al. (2019). Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing, 333: 145–156.
Westermann P, Evins R (2019). Surrogate modelling for sustainable building design—A review. Energy and Buildings, 198: 170–186.
Westermann P, Welzel M, Evins R (2020). Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones. Applied Energy, 278: 115563.
Yadav AK, Chandel SS (2014). Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and Sustainable Energy Reviews, 33: 772–781.
Zarzalejo LF, Ramirez L, Polo J (2005). Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index. Energy, 30: 1685–1697.
Zhang Z, Chong A, Pan Y, et al. (2018). A deep reinforcement learning approach to using whole building energy model for hvac optimal control. In: Proceedings of ASHRAE/IBPSA-USA Building Performance Analysis Conference, Chicago, IL, USA.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Han, J.M., Choi, E.S. & Malkawi, A. CoolVox: Advanced 3D convolutional neural network models for predicting solar radiation on building facades. Build. Simul. 15, 755–768 (2022). https://doi.org/10.1007/s12273-021-0837-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12273-021-0837-0