Abstract
Surrogate modeling has brought about a revolution in computation in the branches of science and engineering. Backed by Artificial Intelligence, a surrogate model can present highly accurate results with a significant reduction in computation time than computer simulation of actual models. Surrogate modeling techniques have found their use in numerous branches of science and engineering, energy system modeling being one of them. Since the idea of hybrid and sustainable energy systems is spreading rapidly in the modern world for the paradigm of the smart energy shift, researchers are exploring the future application of artificial intelligence-based surrogate modeling in analyzing and optimizing hybrid energy systems. One of the promising technologies for assessing applicability for the energy system is the digital twin, which can leverage surrogate modeling. This work presents a comprehensive framework/review on Artificial Intelligence-driven surrogate modeling and its applications with a focus on the digital twin framework and energy systems. The role of machine learning and artificial intelligence in constructing an effective surrogate model is explained. After that, different surrogate models developed for different sustainable energy sources are presented. Finally, digital twin surrogate models and associated uncertainties are described.
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References
R. Alizadeh, J.K. Allen, F. Mistree, Managing computational complexity using surrogate models: a critical review. Res. Eng. Des. 31(3), 275–298 (2020). https://doi.org/10.1007/S00163-020-00336-7
N. Ban, W. Yamazaki, Efficient global optimization method via clustering/classification methods and exploration strategy. Optim. Eng. 22(1), 521–553 (2021)
R. Baños et al., Optimization methods applied to renewable and sustainable energy: a review. Renew. Sust. Energ. Rev. 15(4), 1753–1766 (2011). https://doi.org/10.1016/J.RSER.2010.12.008
G.A. Banyay, M.D. Shields, J.C. Brigham, Efficient global sensitivity analysis for flow-induced vibration of a nuclear reactor assembly using Kriging surrogates. Nucl. Eng. Des. 341, 1–15 (2019). https://doi.org/10.1016/J.NUCENGDES.2018.10.013
T. Barlas et al., Surrogate-based aeroelastic design optimization of tip extensions on a modern 10 MW wind turbine. Wind Energy Sci. 6(2), 491–504 (2021). https://doi.org/10.5194/WES-6-491-2021
N. Bartoli, Optimisation adaptative basée sur les métamodeles (Université Toulouse III, 2019)
A. Berrada, K. Loudiyi, R. El Mrabet, Introduction to hybrid energy systems, in Hybrid Energy System Models, (Elsevier, 2021), pp. 1–43
M.A. Bouhlel, J.R.R.A. Martins, Gradient-enhanced kriging for high-dimensional problems. Eng. Comput. 35(1), 157–173 (2019). https://doi.org/10.1007/S00366-018-0590-X/TABLES/8
M.A. Bouhlel et al., An improved approach for estimating the hyperparameters of the Kriging model for high-dimensional problems through the partial least squares method. Math. Probl. Eng. (2016a). https://doi.org/10.1155/2016/6723410
M.A. Bouhlel et al., Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction. Struct. Multidiscip. Optim. 53(5), 935–952 (2016b)
M.A. Bouhlel et al., A Python surrogate modeling framework with derivatives. Adv. Eng. Softw. 135, 102662 (2019)
J.M. Bourinet, Rare-event probability estimation with adaptive support vector regression surrogates. Reliab. Eng. Syst. Saf. 150, 210–221 (2016). https://doi.org/10.1016/J.RESS.2016.01.023
R.G. Brereton, G.R. Lloyd, Support Vector Machines for classification and regression. Analyst 135(2), 230–267 (2010). https://doi.org/10.1039/B918972F
BYJU’S, Least Square Method. Available at: https://byjus.com/maths/least-square-method/. Accessed 11 Mar 2022 (2022).
D. Cevasco, S. Koukoura, A.J. Kolios, Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications. Renew. Sust. Energ. Rev. 136, 110414 (2021)
S. Chakraborty, G. Bhattacharya, Proceedings of the International Symposium on Engineering Under Uncertainty: Safety Assessment and Management (ISEUSAM-2012) (Springer, 2013)
S. Chakraborty, S. Adhikari, R. Ganguli, The role of surrogate models in the development of digital twins of dynamic systems. Appl. Math. Model. 90, 662–681 (2021). https://doi.org/10.1016/J.APM.2020.09.037
S.J.S. Chelladurai et al., Optimization of process parameters using response surface methodology: a review. Mater. Today Proc. 37, 1301–1304 (2021)
Q. Chen et al., Feasibility analysis of nuclear–coal hybrid energy systems from the perspective of low-carbon development. Appl. Energy 158, 619–630 (2015)
S. Cho et al., Optimization of an explosive waste incinerator via an artificial neural network surrogate model. Chem. Eng. J. 407, 126659 (2021). https://doi.org/10.1016/J.CEJ.2020.126659
I. Cruz-Vega et al., Surrogate modeling based on an adaptive network and granular computing. Soft. Comput. 20(4), 1549–1563 (2016)
S.E. Davis, S. Cremaschi, M.R. Eden, Efficient surrogate model development: impact of sample size and underlying model dimensions, in Computer Aided Chemical Engineering, (Elsevier, 2018), pp. 979–984
J. Eason, S. Cremaschi, Adaptive sequential sampling for surrogate model generation with artificial neural networks. Comput. Chem. Eng. 68, 220–232 (2014)
R. Evins, A review of computational optimisation methods applied to sustainable building design. Renew. Sust. Energ. Rev. 22, 230–245 (2013). https://doi.org/10.1016/J.RSER.2013.02.004
K. Ezhilsabareesh, S.H. Rhee, A. Samad, Shape optimization of a bidirectional impulse turbine via surrogate models. Eng. Appl. Comput. Fluid Mech. 12(1), 1–12 (2017). https://doi.org/10.1080/19942060.2017.1330709
C.M. Frenz, Possibilities and limitations of computer simulation. IEEE Potentials 26(2), 30–33 (2007)
R. Ganguli, S. Adhikari, The digital twin of discrete dynamic systems: Initial approaches and future challenges. Appl. Math. Model. 77, 1110–1128 (2020). https://doi.org/10.1016/J.APM.2019.09.036
T. Goel et al., Ensemble of surrogates. Struct. Multidiscip. Optim. 33(3), 199–216 (2007)
D. Gorissen et al., A surrogate modeling and adaptive sampling toolbox for computer based design. J. Mach. Learn. Res. Cambridge, MA 11, 2051–2055 (2010)
A. Gupta, R.P. Saini, M.P. Sharma, Modelling of hybrid energy system – Part I: Problem formulation and model development. Renew. Energy 36(2), 459–465 (2011)
T. Hastie, R. Tibshirani, J. Friedman, Linear methods for regression. The elements of statistical learning: data mining, in Conference and prediction. Springer Series in Statistics, (Springer, 2001)
C.A. Henao, C.T. Maravelias, Surrogate-based process synthesis, in Computer Aided Chemical Engineering, (Elsevier, 2010), pp. 1129–1134
G. Jacobsen, On the Path to a Nuclear Fuel Digital Twin: Modeling and Simulation of Silicon Carbide Cladding for Accelerated Fuel Qualification (US Department of Energy, 2022) Available at: https://www.energy.gov/sites/default/files/2021-11/ne-abstract-silicon-112321.pdf
P. Jiang, Q. Zhou, X. Shao, Surrogate Model-Based Engineering Design and Optimization (2020). https://doi.org/10.1007/978-981-15-0731-1
X. Ju, F. Liu, Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation. Appl. Energy 248, 429–445 (2019). https://doi.org/10.1016/J.APENERGY.2019.04.084
M. Kaya, S. Hajimirza, Application of artificial neural network for accelerated optimization of ultra thin organic solar cells. Sol. Energy 165, 159–166 (2018a). https://doi.org/10.1016/J.SOLENER.2018.02.062
M. Kaya, S. Hajimirza, Rapid optimization of external quantum efficiency of thin film solar cells using surrogate modeling of absorptivity. Sci. Rep. 8(1), 1–9 (2018b). https://doi.org/10.1038/s41598-018-26469-3
S.W. Kim et al., A time-dependent surrogate model for storm surge prediction based on an artificial neural network using high-fidelity synthetic hurricane modeling. Nat. Hazards 76(1), 565–585 (2015). https://doi.org/10.1007/S11069-014-1508-6/FIGURES/9
B. Kochunas, X. Huan, Digital twin concepts with uncertainty for nuclear power applications. Energies 14(14), 4235 (2021)
D. Kumar et al., Influence of nuclear data parameters on integral experiment assimilation using Cook’s distance, in EPJ Web of Conferences, (EDP Sciences, 2019), p. 7001
D. Kumar, Y. Koutsawa, G. Rauchs, et al., Efficient uncertainty quantification and management in the early stage design of composite applications. Compos. Struct. 251, 112538 (2020a). https://doi.org/10.1016/J.COMPSTRUCT.2020.112538
D. Kumar, S.B. Alam, H. Sjöstrand, et al., Nuclear data adjustment using Bayesian inference, diagnostics for model fit and influence of model parameters, in EPJ Web of Conferences, (EDP Sciences, 2020b), p. 13003
D. Kumar, S.B. Alam, D. Vučinić, et al., Uncertainty quantification and robust optimization in engineering, in Advances in Visualization and Optimization Techniques for Multidisciplinary Research, (Springer, 2020c), pp. 63–93
D. Kumar et al., Quantitative risk assessment of a high power density small modular reactor (SMR) core using uncertainty and sensitivity analyses. Energy 227, 120400 (2021)
D. Kumar et al., Multi-criteria decision making under uncertainties in composite materials selection and design. Compos. Struct. 279, 114680 (2022)
M.A.D. Larsen et al., Challenges of data availability: analysing the water-energy nexus in electricity generation. Energy Strategy Rev. 26, 100426 (2019)
V. Le, L. Caracoglia, A neural network surrogate model for the performance assessment of a vertical structure subjected to non-stationary, tornadic wind loads. Comput. Struct. 231, 106208 (2020). https://doi.org/10.1016/J.COMPSTRUC.2020.106208
P. Lopion et al., A review of current challenges and trends in energy systems modeling. Renew. Sust. Energ. Rev. 96, 156–166 (2018). https://doi.org/10.1016/J.RSER.2018.07.045
H. Lund, Renewable energy strategies for sustainable development. Energy 32(6), 912–919 (2007). https://doi.org/10.1016/J.ENERGY.2006.10.017
M.A. Mahmood et al., Artificial neural network algorithms for 3D printing. Materials 14(1), 163 (2020). https://doi.org/10.3390/MA14010163
S. Mahulja, G.C. Larsen, A. Elham, Engineering an optimal wind farm using surrogate models. Wind Energy 21(12), 1296–1308 (2018). https://doi.org/10.1002/WE.2255
A.K. Nag, S. Sarkar, Modeling of hybrid energy system for futuristic energy demand of an Indian rural area and their optimal and sensitivity analysis. Renew. Energy 118, 477–488 (2018)
S. Negi, L. Mathew, Hybrid renewable energy system: a review. Int. J. Electron. Electr. Eng. 7(5), 535–542 (2014)
J. Nowotny et al., Towards sustainable energy. Generation of hydrogen fuel using nuclear energy. Int. J. Hydrog. Energy 41(30), 12812–12825 (2016)
W.L. Oberkampf, T.G. Trucano, C. Hirsch, Verification, validation, and predictive capability in computational engineering and physics. Appl. Mech. Rev. 57(5), 345–384 (2004)
J.M. Pearce, Limitations of nuclear power as a sustainable energy source. Sustainability 4(6), 1173–1187 (2012)
F. Pedregosa et al., Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
S. Peitz, M. Dellnitz, A survey of recent trends in multiobjective optimal control – surrogate models, feedback control and objective reduction. Math. Comput. Appl. 23(2), 30 (2018)
X. Peng et al., Design and analysis of concentrating solar power plants with fixed-bed reactors for thermochemical energy storage. Appl. Energy 262, 114543 (2020). https://doi.org/10.1016/J.APENERGY.2020.114543
A.T.D. Perera et al., Optimum design of distributed energy hubs using hybrid surrogate models (HSM). Energy Procedia 122, 187–192 (2017). https://doi.org/10.1016/J.EGYPRO.2017.07.343
A.T.D. Perera et al., Machine learning methods to assist energy system optimization. Appl. Energy 243, 191–205 (2019)
A. Qazi et al., Towards sustainable energy: a systematic review of renewable energy sources, technologies, and public opinions. IEEE Access 7, 63837–63851 (2019)
P. Qiao et al., Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing. Environ. Sci. Pollut. Res. 25(16), 15597–15608 (2018). https://doi.org/10.1007/S11356-018-1552-Y/FIGURES/5
M.I. Radaideh, T. Kozlowski, Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling. Int. J. Energy Res. 43(14), 7866–7890 (2019). https://doi.org/10.1002/ER.4698
G. Ruan et al., Review of learning-assisted power system optimization. CSEE J. Power Energy Syst. 7(2), 221–231 (2020)
M.F. Ruth et al., Nuclear-renewable hybrid energy systems: opportunities, interconnections, and needs. Energy Convers. Manag. 78, 684–694 (2014)
A.M. Schweidtmann, A. Mitsos, Deterministic global optimization with artificial neural networks embedded. J. Optim. Theory Appl. 180(3), 925–948 (2019)
S. Shan et al., Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions. Struct. Multidiscip. Optim. 41(2), 219–241 (2009). https://doi.org/10.1007/S00158-009-0420-2
M. Shi et al., A multi-fidelity surrogate model based on support vector regression. Struct. Multidiscip. Optim. 61(6), 2363–2375 (2020). https://doi.org/10.1007/S00158-020-02522-6/FIGURES/11
A. Sobester, A. Forrester, A. Keane, Engineering Design via Surrogate Modelling: A Practical Guide (Wiley, 2008)
A.R. Starke et al., Multi-objective optimization of hybrid CSP+PV system using genetic algorithm. Energy 147, 490–503 (2018). https://doi.org/10.1016/J.ENERGY.2017.12.116
J. Straus, S. Skogestad, Surrogate model generation using self-optimizing variables. Comput. Chem. Eng. 119, 143–151 (2018)
M. Sun et al., Probabilistic short-term wind forecasting based on pinball loss optimization, in 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 – Proceedings, (2018). https://doi.org/10.1109/PMAPS.2018.8440347
M. Sun et al., A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization. Appl. Energy 238, 1497–1505 (2019). https://doi.org/10.1016/J.APENERGY.2019.01.182
M.R. Sunny et al., An artificial neural network residual kriging based surrogate model for shape and size optimization of a stiffened panel, in 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, (2013). https://doi.org/10.2514/6.2013-1689
D. Torregrossa et al., Energy saving in WWTP: daily benchmarking under uncertainty and data availability limitations. Environ. Res. 148, 330–337 (2016)
P. Tsirikoglou et al., A hyperparameters selection technique for support vector regression models. Appl. Soft Comput. 61, 139–148 (2017). https://doi.org/10.1016/J.ASOC.2017.07.017
C.C. Tutum, K. Deb, A Multimodal Approach for Evolutionary Multi-objective Optimization (MEMO): proof-of-principle results. Lect. Notes Comput. Sci 9018, 3–18 (2015). https://doi.org/10.1007/978-3-319-15934-8\_1
F.A.C. Viana et al., Efficient global optimization algorithm assisted by multiple surrogate techniques. J. Glob. Optim. 56(2), 669–689 (2012). https://doi.org/10.1007/S10898-012-9892-5
E. Vine, Breaking down the silos: the integration of energy efficiency, renewable energy, demand response and climate change. Energy Effic. 1(1), 49–63 (2008). https://doi.org/10.1007/S12053-008-9004-Z
E. Winsberg, Science in the Age of Computer Simulation (University of Chicago Press, 2010)
E. Winsberg, Computer Simulations in Science. (2013)
H. Xiang et al., An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers. Struct. Multidiscip. Optim. 55(2), 701–713 (2017). https://doi.org/10.1007/S00158-016-1528-9/FIGURES/11
S. Yoon et al., Accelerated system-level seismic risk assessment of bridge transportation networks through artificial neural network-based surrogate model. Appl. Sci. 10(18), 6476 (2020). https://doi.org/10.3390/APP10186476
X. Zhang et al., Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization. Comput. Methods Appl. Mech. Eng. 373, 113485 (2021a). https://doi.org/10.1016/J.CMA.2020.113485
Z. Zhang et al., Applied research on InSAR and GPS data fusion in deformation monitoring. Sci. Program. 2021 (2021b)
Y. Zhou, S. Zheng, Multi-level uncertainty optimisation on phase change materials integrated renewable systems with hybrid ventilations and active cooling. Energy 202, 117747 (2020). https://doi.org/10.1016/J.ENERGY.2020.117747
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Khan, A.H., Omar, S., Mushtary, N., Verma, R., Kumar, D., Alam, S. (2022). Digital Twin and Artificial Intelligence Incorporated with Surrogate Modeling for Hybrid and Sustainable Energy Systems. In: Fathi, M., Zio, E., Pardalos, P.M. (eds) Handbook of Smart Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-72322-4_147-1
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