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Articles

Condition-based monitoring as a robust strategy towards sustainable and resilient multi-energy infrastructure systems

, , , &
Pages 170-189 | Received 26 Aug 2022, Accepted 06 Oct 2022, Published online: 18 Oct 2022

References

  • Abdullah, F. B., Iqbal, R., Ahmad, S., El-Affendi, M. A., & Kumar, P. (2022). Optimization of multidimensional energy security: An index based assessment. Energies, 15(11), 3929. https://doi.org/10.3390/en15113929
  • Abeysekera, M. (2016). Combined analysis of coupled energy networks. Cardiff University].
  • Abouhamad, M., Dawood, T., Jabri, A., Alsharqawi, M., & Zayed, T. (2017). Corrosiveness mapping of bridge decks using image-based analysis of GPR data. Automation in Construction, 80, 104–117. https://doi.org/10.1016/j.autcon.2017.03.004
  • Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On predictive maintenance in industry 4.0: Overview, models, and challenges. Applied Sciences, 12(16), 8081. https://doi.org/10.3390/app12168081
  • Adams, T. M., Bekkem, K. R., & Toledo-Durán, E. J. J. J. O. T. E. (2012). Freight resilience measures. 138(11), 1403–1409. doi:10.1061/(ASCE)TE.1943-5436.0000415.
  • Afrin, T., & Yodo, N. (2019). A concise survey of advancements in recovery strategies for resilient complex networks. Journal of Complex Networks, 7(3), 393–420. https://doi.org/10.1093/comnet/cny025
  • Afrin, T., & Yodo, N. (2022a). A hybrid recovery strategy toward sustainable infrastructure systems. Journal of Infrastructure Systems, 28(1), 04021054. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000670
  • Afrin, T., & Yodo, N. (2022b). A Long Short-Term Memory-based correlated traffic data prediction framework. Knowledge-Based Systems, 237, 107755. https://doi.org/10.1016/j.knosys.2021.107755
  • Ahmad, M. W., Reynolds, J., & Rezgui, Y. (2018). Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees. Journal of Cleaner Production, 203, 810–821. https://doi.org/10.1016/j.jclepro.2018.08.207
  • Asadi, E., Salman, A. M., Li, Y., & Yu, X. (2021). Localized health monitoring for seismic resilience quantification and safety evaluation of smart structures. Structural Safety, 93, 102127. https://doi.org/10.1016/j.strusafe.2021.102127
  • Aydin, N. Y., Duzgun, H. S., Heinimann, H. R., Wenzel, F., & Gnyawali, K. R. (2018). Framework for improving the resilience and recovery of transportation networks under geohazard risks. International Journal of Disaster Risk Reduction, 31, 832–843. https://doi.org/10.1016/j.ijdrr.2018.07.022
  • Ayyub, B. M. (2015). Practical resilience metrics for planning, design, and decision making. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 1(3), 04015008. https://doi.org/10.1061/AJRUA6.0000826
  • Ayyub, B. M. (2020). Infrastructure resilience and sustainability: Definitions and relationships. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 6(3), 02520001. https://doi.org/10.1061/AJRUA6.0001067
  • Bagherian, M. A., Mehranzamir, K., Pour, A. B., Rezania, S., Taghavi, E., Nabipour-Afrouzi, H., Dalvi-Esfahani, M., & Alizadeh, S. M. (2021). Classification and analysis of optimization techniques for integrated energy systems utilizing renewable energy sources: A review for CHP and CCHP systems. Processes, 9(2), 339. https://doi.org/10.3390/pr9020339
  • Betti, A., Tucci, M., Crisostomi, E., Piazzi, A., Barmada, S., & Thomopulos, D. (2021). Fault prediction and early-detection in large pv power plants based on self-organizing maps. Sensors, 21(5), 1687. https://doi.org/10.3390/s21051687
  • Bhattacharyya, S. C. (2019). Energy economics: Concepts, issues, markets and governance. Springer Nature.
  • Blatter, B. (2021). Condition-based monitoring: The future of machine maintenance https://www.itiretailservices.citibankonline.com/RSnextgen/svc/launch/index.action?siteId=PLCN_BESTBUY&langId=en_US&pagename=authenticate#dashboard
  • Blondeel, M., Bradshaw, M. J., Bridge, G., & Kuzemko, C. (2021). The geopolitics of energy system transformation: A review. Geography Compass, 15(7), e12580. https://doi.org/10.1111/gec3.12580
  • Bruckner, T., Bashmakov, I. A., Mulugetta, Y., Chum, H., De la Vega Navarro, A., Edmonds, J., Faaij, A., Fungtammasan, B., Garg, A., & Hertwich, E., Honnery, D., Infield, M., Kainuma, S., Khennas, S., Kim, H.B., Nimir, K., Riahi, N., Strachan, R., Wiser, and Zhang, X. (2014). Energy Systems. In, Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.), Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA https://pure.iiasa.ac.at/id/eprint/11118/
  • Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O’Rourke, T. D., Reinhorn, A. M., Shinozuka, M., Tierney, K., Wallace, W. A., & Winterfeldt, D. V. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 19(4), 733–752. https://doi.org/10.1193/1.1623497
  • Busby, J. W., Baker, K., Bazilian, M. D., Gilbert, A. Q., Grubert, E., Rai, V., Rhodes, J. D., Shidore, S., Smith, C. A., & Webber, M. E. (2021). Cascading risks: Understanding the 2021 winter blackout in Texas. Energy Research & Social Science, 77, 102106. https://doi.org/10.1016/j.erss.2021.102106
  • Carradore, L., & Turri, R. (2009). Modeling and simulation of multi-vector energy systems. (Ed.),^(Eds.). IEEE Bucharest PowerTech.
  • Chauhan, A., & Saini, R. (2014). A review on integrated renewable energy system based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control. Renewable and Sustainable Energy Reviews, 38, 99–120. https://doi.org/10.1016/j.rser.2014.05.079
  • Chen, Z., Chen, Y., Wu, L., Cheng, S., & Lin, P. (2019). Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Conversion and Management, 198, 111793. https://doi.org/10.1016/j.enconman.2019.111793
  • Cohen, A. (2021a). Hurricane Ida Puts America’s energy security to the test. https://www.forbes.com/sites/arielcohen/2021/09/01/hurricane-ida-puts-americas-energy-security-to-the-test/?sh=18fdd3d24c99
  • Cohen, A. (2021b). Texas energy crisis is an Epic resilience and leadership failure. https://www.forbes.com/sites/arielcohen/2021/02/19/texas-energy-crisis-is-an-epic-resilience-and-leadership-failure/?sh=3eb05e546eee
  • Davoudi, M., Jooshaki, M., Moeini-Aghtaie, M., Barmayoon, M. H., & Aien, M. (2022). Developing a multi-objective multi-layer model for optimal design of residential complex energy systems. International Journal of Electrical Power & Energy Systems, 138, 107889. https://doi.org/10.1016/j.ijepes.2021.107889
  • de Azevedo, H. D. M., Araújo, A. M., & Bouchonneau, N. (2016). A review of wind turbine bearing condition monitoring: State of the art and challenges. Renewable and Sustainable Energy Reviews, 56, 368–379. https://doi.org/10.1016/j.rser.2015.11.032
  • Ekic, A., Wu, D., & Huang, Y. (2022). A review on cascading failure Analysis for integrated power and gas systems. (Ed.),^(Eds.) 2022 IEEE 7th International Energy Conference (ENERGYCON).
  • Furse, C. M., Kafal, M., Razzaghi, R., & Shin, Y.-J. (2020). Fault diagnosis for electrical systems and power networks: A review. IEEE Sensors Journal, 21(2), 888–906. https://doi.org/10.1109/JSEN.2020.2987321
  • Galvan, G., & Agarwal, J. (2020). Assessing the vulnerability of infrastructure networks based on distribution measures. Reliability Engineering & System Safety, 196, 106743. https://doi.org/10.1016/j.ress.2019.106743
  • Gangsar, P., & Tiwari, R. (2020). Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mechanical Systems and Signal Processing, 144, 106908. https://doi.org/10.1016/j.ymssp.2020.106908
  • García Márquez, F. P. (2022). . Energies,15(7), 2499. MDPI.
  • Geman, B., & Freedman, A. (2021). Hurricane Ida exposes America’s precarious energy infrastructure. https://www.axios.com/2021/08/31/hurricane-ida-infrastructure-vulnerabilities
  • Goswami, D. Y., & Kreith, F. (2007). Energy conversion. CRC press.
  • Grijalva, S. (2017). Multi-dimensional, multi-scale modeling and algorithms for integrating variable energy resources in power networks: Challenges and opportunities. Renewable Energy Integration, 41–53. doi:10.1016/B978-0-12-809592-8.00004-4.
  • Group, P. (2020). The Importance of Condition Based Monitoring. https://www.prometheusgroup.com/posts/the-importance-of-condition-based-monitoring
  • Haes Alhelou, H., Hamedani-Golshan, M. E., Njenda, T. C., & Siano, P. (2019). A survey on power system blackout and cascading events: Research motivations and challenges. Energies, 12(4), 682. https://doi.org/10.3390/en12040682
  • Hauser, C. H., Bakken, D. E., & Bose, A. (2005). A failure to communicate: Next generation communication requirements, technologies, and architecture for the electric power grid. IEEE Power and Energy Magazine, 3(2), 47–55. https://doi.org/10.1109/MPAE.2005.1405870
  • Hossain, M. L., Abu-Siada, A., & Muyeen, S. (2018). Methods for advanced wind turbine condition monitoring and early diagnosis: A literature review. Energies, 11(5), 1309. https://doi.org/10.3390/en11051309
  • Hosseini, S. H. R., Allahham, A., Vahidinasab, V., Walker, S. L., & Taylor, P. (2021). Techno-economic-environmental evaluation framework for integrated gas and electricity distribution networks considering impact of different storage configurations. International Journal of Electrical Power & Energy Systems, 125, 106481. https://doi.org/10.1016/j.ijepes.2020.106481
  • Hosseini, S. H. R., Allahham, A., Walker, S. L., & Taylor, P. (2020). Optimal planning and operation of multi-vector energy networks: A systematic review. Renewable and Sustainable Energy Reviews, 133, 110216. https://doi.org/10.1016/j.rser.2020.110216
  • Karad, S., & Thakur, R. (2021). Efficient monitoring and control of wind energy conversion systems using Internet of things (IoT): A comprehensive review. Environment, Development and Sustainability, 23(10), 14197–14214. https://doi.org/10.1007/s10668-021-01267-6
  • Katsaprakakis, D. A., Papadakis, N., & Ntintakis, I. (2021). A comprehensive analysis of wind turbine blade damage. Energies, 14(18), 5974. https://doi.org/10.3390/en14185974
  • Khumprom, P., & Yodo, N. (2019). A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies, 12(4), 660. https://doi.org/10.3390/en12040660
  • Li, B., Delpha, C., Diallo, D., & Migan-Dubois, A. (2021). Application of artificial neural networks to photovoltaic fault detection and diagnosis: A review. Renewable and Sustainable Energy Reviews, 138, 110512. https://doi.org/10.1016/j.rser.2020.110512
  • Liu, W., & Song, Z. (2020). Review of studies on the resilience of urban critical infrastructure networks. Reliability Engineering & System Safety, 193, 106617. https://doi.org/10.1016/j.ress.2019.106617
  • Lombardi, F., Rocco, M. V., & Colombo, E. (2019). A multi-layer energy modelling methodology to assess the impact of heat-electricity integration strategies: The case of the residential cooking sector in Italy. Energy, 170, 1249–1260. https://doi.org/10.1016/j.energy.2019.01.004
  • Lounis, Z., & McAllister, T. P. (2016). Risk-based decision making for sustainable and resilient infrastructure systems. Journal of Structural Engineering, 142(9), F4016005. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001545
  • Luo, H., Alkhaleel, B. A., Liao, H., & Pascual, R. (2021). Resilience improvement of a critical infrastructure via optimal replacement and reordering of critical components. Sustainable and Resilient Infrastructure, 6(1–2), 73–93. https://doi.org/10.1080/23789689.2019.1710072
  • Min, Z., Muqing, W., Lilin, Q., Quanbiao, A., & Sixu, L. (2021). Evaluation of cross-layer network vulnerability of power communication network based on multi-dimensional and multi-layer node importance analysis. IEEE Access, 10, 67181–67197. https://doi.org/10.1109/ACCESS.2021.3109902
  • Mosheiov, G., & Sarig, A. (2009). Scheduling a maintenance activity to minimize total weighted completion-time. Computers & Mathematics with Applications, 57(4), 619–623. https://doi.org/10.1016/j.camwa.2008.11.008
  • Nazari-heris, M., Jabari, F., Mohammadi-ivatloo, B., Asadi, S., & Habibnezhad, M. (2020). An updated review on multi-carrier energy systems with electricity, gas, and water energy sources. Journal of Cleaner Production, 275, 123136. https://doi.org/10.1016/j.jclepro.2020.123136
  • O’Malley, M. J., Anwar, M. B., Heinen, S., Kober, T., McCalley, J., McPherson, M., Muratori, M., Orths, A., Ruth, M., & Schmidt, T. J. (2020). Multicarrier energy systems: Shaping our energy future. Proceedings of the IEEE, 108(9), 1437–1456. https://doi.org/10.1109/JPROC.2020.2992251
  • Ouyang, M., Dueñas-Osorio, L., & Min, X. (2012). A three-stage resilience analysis framework for urban infrastructure systems. Structural Safety, 36, 23–31. https://doi.org/10.1016/j.strusafe.2011.12.004
  • Paul, L. (2022). Oil and Gas Pipeline Cybersecurity. Tex. J. Oil Gas & Energy L, 17, 38. https://heinonline.org/HOL/LandingPage?handle=hein.journals/texjogel17&div=10&id=&page=
  • Poudineh, R. (2022). Energy networks in the energy transition era. Oxford Institute for Energy Studies. https://ora.ox.ac.uk/objects/uuid:9b3744c5-cfab-4c22-b866-e36360ecd5f2
  • Praminta, S. M., Wiguna, S., & Pramana, A. (2020). Blackout restoration plan in jakarta power grid. (Ed.),^(Eds.). 2020 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP).
  • Pushpa, S. (2019). Major Blackouts in The World and Lessons Learned. Water and Energy International, 62(5), 28–34. https://www.indianjournals.com/ijor.aspx?target=ijor:wei&volume=62r&issue=5&article=007
  • Rashid, H., Khalaji, E., Rasheed, J., & Batunlu, C. (2020). Fault prediction of wind turbine gearbox based on SCADA data and machine learning. (Ed.),^(Eds.). 2020 10th International Conference on Advanced Computer Information Technologies (ACIT).
  • Renugadevi, N., Saravanan, S., & Sudha, C. N. (2021). IoT based smart energy grid for sustainable cites. Materials Today: Proceedings.
  • Reynolds, J., Ahmad, M. W., & Rezgui, Y. (2018). Holistic modelling techniques for the operational optimisation of multi-vector energy systems. Energy and Buildings, 169, 397–416. https://doi.org/10.1016/j.enbuild.2018.03.065
  • Rudin, C., Waltz, D., Anderson, R. N., Boulanger, A., Salleb-Aouissi, A., Chow, M., Dutta, H., Gross, P. N., Huang, B., Ierome, S., Isaac, D. F., Kressner, A., Passonneau, R. J., Radeva, A., & Wu, L. (2011). Machine learning for the New York City power grid. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(2), 328–345. https://doi.org/10.1109/TPAMI.2011.108
  • Samsatli, S., & Samsatli, N. J. (2018). A multi-objective MILP model for the design and operation of future integrated multi-vector energy networks capturing detailed spatio-temporal dependencies. Applied Energy, 220, 893–920. https://doi.org/10.1016/j.apenergy.2017.09.055
  • Saravanan, D., Hasan, A., Singh, A., Mansoor, H., & Shaw, R. N. (2020). Fault prediction of transformer using machine learning and DGA. (Ed.),^(Eds.). 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON).
  • Schaeffer, R., Szklo, A. S., de Lucena, A. F. P., Borba, B. S. M. C., Nogueira, L. P. P., Fleming, F. P., Troccoli, A., Harrison, M., & Boulahya, M. S. (2012). Energy sector vulnerability to climate change: A review. Energy, 38(1), 1–12. https://doi.org/10.1016/j.energy.2011.11.056
  • Shafie-Khah, M., & Catalão, J. P. (2014). A stochastic multi-layer agent-based model to study electricity market participants behavior. IEEE Transactions on Power Systems, 30(2), 867–881. https://doi.org/10.1109/TPWRS.2014.2335992
  • Shaheen, A. M., Elsayed, A. M., El-Sehiemy, R. A., Ghoneim, S. S., Alharthi, M. M., & Ginidi, A. R. (2022). Multi-dimensional energy management based on an optimal power flow model using an improved quasi-reflection jellyfish optimization algorithm. Engineering Optimization, 1–23. https://doi.org/10.1080/0305215X.2022.2051021
  • Shin, J.-H., & Jun, H.-B. (2015). On condition based maintenance policy. Journal of Computational Design and Engineering, 2(2), 119–127. https://doi.org/10.1016/j.jcde.2014.12.006
  • Skydt, M. R., Bang, M., & Shaker, H. R. (2021). A probabilistic sequence classification approach for early fault prediction in distribution grids using long short-term memory neural networks. Measurement, 170, 108691. https://doi.org/10.1016/j.measurement.2020.108691
  • Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J., & Nenadic, G. (2019). Machine learning methods for wind turbine condition monitoring: A review. Renewable Energy, 133, 620–635. https://doi.org/10.1016/j.renene.2018.10.047
  • Sun, H., Yang, M., & Wang, H. (2022). Resilience-based approach to maintenance asset and operational cost planning. Process Safety and Environmental Protection, 162, 987–997. https://doi.org/10.1016/j.psep.2022.05.002
  • Taylor, P., Abeysekera, M., Bian, Y., Ćetenović, D., Deakin, M., Ehsan, A., Levi, V., Li, F., Oduro, R., & Preece, R. (2022). An interdisciplinary research perspective on the future of multi-vector energy networks. International Journal of Electrical Power & Energy Systems, 135, 107492. https://doi.org/10.1016/j.ijepes.2021.107492
  • Tchakoua, P., Wamkeue, R., Ouhrouche, M., Slaoui-Hasnaoui, F., Tameghe, T. A., & Ekemb, G. (2014). Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies, 7(4), 2595–2630. https://doi.org/10.3390/en7042595
  • Trojan, F., & Marçal, R. (2016). Sorting maintenance types by multi-criteria analysis to clarify maintenance concepts in POM. (Ed.),^(Eds.) Production and Operations Management Society 27th Annual Conference.
  • U.S. Energy Information Administration. (2022). U.S. energy facts explained: The United States uses a mix of energy sources https://www.eia.gov/energyexplained/us-energy-facts/
  • Utami, C. N., & Hartono, D. (2022). A multidimensional energy poverty in Indonesia and its impact on health. International Energy Journal, 22(2). http://rericjournal.ait.ac.th/index.php/reric/article/view/2710
  • Van Aalst, M. K. (2006). The impacts of climate change on the risk of natural disasters. Disasters, 30(1), 5–18. https://doi.org/10.1111/j.1467-9523.2006.00303.x
  • Wang, J., Liang, Y., Zheng, Y., Gao, R. X., & Zhang, F. (2020). An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples. Renewable Energy, 145, 642–650. https://doi.org/10.1016/j.renene.2019.06.103
  • Wen, M., Chen, Y., Yang, Y., Kang, R., & Zhang, Y. (2020). Resilience‐based component importance measures. International Journal of Robust and Nonlinear Control, 30(11), 4244–4254. https://doi.org/10.1002/rnc.4813
  • Whitson, C., & Ramirez-Marquez, J. E. (2009). Resiliency as a component importance measure in network reliability. Reliability Engineering and System Safety, 94(1685–1693), 1685–1693. https://doi.org/10.1016/j.ress.2009.05.001
  • Wohlin, C., Kalinowski, M., Felizardo, K. R., & Mendes, E. (2022). Successful combination of database search and snowballing for identification of primary studies in systematic literature studies. Information and Software Technology, 147, 106908. https://doi.org/10.1016/j.infsof.2022.106908
  • Wu J and Wang P. (2021). Post-disruption performance recovery to enhance resilience of interconnected network systems. Sustainable and Resilient Infrastructure, 6(1–2), 107–123. 10.1080/23789689.2019.1710073
  • Xiang, L., Wang, P., Yang, X., Hu, A., & Su, H. (2021). Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism. Measurement, 175, 109094. https://doi.org/10.1016/j.measurement.2021.109094
  • Xiao, J., Li, C., Liu, B., Huang, J., & Xie, L. (2022). Prediction of wind turbine blade icing fault based on selective deep ensemble model. Knowledge-Based Systems, 242, 108290. https://doi.org/10.1016/j.knosys.2022.108290
  • Yang, S.-L., Ma, Y., Xu, D.-L., & Yang, J.-B. (2011). Minimizing total completion time on a single machine with a flexible maintenance activity. Computers & Operations Research, 38(4), 755–770. https://doi.org/10.1016/j.cor.2010.09.003
  • Yodo, N., & Goethals, P. L. (2019). Cybersecurity management via control strategies for resilient cyber-physical systems. (Ed.),^(Eds.) IIE Annual Conference. Proceedings.
  • Yodo, N., & Wang, P. (2016). Engineering resilience quantification and system design implications: A literature Survey. Journal of Mechanical Design, 138(111408), 1–13. https://doi.org/10.1115/1.4034223
  • Yodo, N., & Wang, P. (2018). A control-guided failure restoration framework for the design of resilient engineering systems. Reliability Engineering & System Safety, 178, 179–190. https://doi.org/10.1016/j.ress.2018.05.018
  • Yodo, N., Wang, P., & Rafi, M. (2017). Enabling resilience of complex engineered systems using control theory. IEEE Transactions on Reliability, 67(1), 53–65. https://doi.org/10.1109/TR.2017.2746754
  • Yuan, P., Zhang, Q., Zhang, T., Chi, C., Zhang, X., Li, P., & Gong, X. (2019). Analysis and enlightenment of the blackouts in Argentina and New York. (Ed.),^(Eds.). Chinese Automation Congress (CAC).
  • Zhang, C., Hu, D., & Yang, T. (2022). Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost. Reliability Engineering & System Safety, 222, 108445. https://doi.org/10.1016/j.ress.2022.108445
  • Zhao, Y., Li, T., Zhang, X., & Zhang, C. (2019). Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renewable and Sustainable Energy Reviews, 109, 85–101. https://doi.org/10.1016/j.rser.2019.04.021
  • Zorn, C. R., & Shamseldin, A. Y. (2015). Post-disaster infrastructure restoration: A comparison of events for future planning. International Journal of Disaster Risk Reduction, 13, 158–166. https://doi.org/10.1016/j.ijdrr.2015.04.004