Optimizing Energy, Empowering Sustainability: A Review of Artificial Intelligence Integration in Engineering Devices

[1] Minoli, D., Sohraby, K., Occhiogrosso, B., 2017. IoT considerations, requirements, and architectures for smart buildings-energy optimization and next-generation building management systems. IEEE Internet Things J 4, 269e283. https:// doi.org/10.1109/JIOT.2017.2647881. [2] Kow, K.W.,Wong, Y.W., Rajkumar, Rajparthiban Kumar, Rajkumar, Rajprasad Kumar, A review on the performance of artificial intelligence and conventional methods in mitigating PV grid-tied related power quality events. Renew. Sustain. Energy Rev. 56, 334e346 [3] Youssef, A., El-Telbany, M., Zekry, A., 2017. The role of artificial intelligence in photovoltaic systems design and control: a review. Renew. Sustain. Energy Rev. 78, 72e79. https://doi.org/10.1016/j.rser.2017.04.046. [4] Seyedmahmoudian, M., Rahmani, R., Mekhilef, S., Maung Than Oo, A., Stojcevski, A., Soon, T.K., Ghandhari, A.S., 2015. Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method. IEEE Trans. Sustain. Energy 6, 850e862. https://doi.org/10.1109/TSTE.2015.2413359. [5] Ranaweera, D.K., Karady, G.G., Farmer, R.G., 1997. Economic impact analysis of load forecasting. IEEE Trans. Power Syst. 12, [6] Kong, W., Dong, Z.Y., Hill, D.J., Luo, F., Xu, Y., 2018. Short-term residential load forecasting based on resident behaviour learning. IEEE Trans. Power Syst. 33 https://doi.org/10.1109/TPWRS.2017.2688178 [7] Rodríguez, F., Florez-Tapia, A.M., Font_an, L., Galarza, A., 2020. Very short-term wind power density forecasting through artificial neural networks for microgrid control. Renew. Energy 145, 1517e1527. https://doi.org/10.1016/ j.renene.2019.07.067. [8] Ren, Y., Suganthan, P.N., Srikanth, N., 2015. A comparative study of empirical mode decomposition based short-term wind speed forecasting methods. IEEE Trans. Sustain. Energy 6, 236e244. https://doi.org/10.1109/TSTE.2014.2365580. [9] Debnath, K.B., Mourshed, M., 2018. Forecasting methods in energy planning models. Renew. Sustain. Energy Rev. 88, 297e325. https://doi.org/10.1016/j.rser.2018.02.002. [10] Guo, Y., Tan, Z., Chen, H., Li, G., Wang, J., Huang, R., Liu, J., Ahmad, T., 2018. Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving. Appl. Energy 225, 732e745. https://doi.org/10.1016/j.apenergy.2018.05.075 [11] Sani, A.S., Yuan, D., Jin, J., Gao, L., Yu, S., Dong, Z.Y., 2019. Cyber security framework for Internet of things-based energy internet. Future Generat. Comput. Syst. 93, 849e859. https://doi.org/10.1016/j.future.2018.01.029. [12] Kaplan, A., Haenlein, M., 2019b. Siri, Siri, in my hand: who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus. Horiz. 62, 15e25. https://doi.org/10.1016/j.bushor.2018.08.004. [13] Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. [14] Stankovic, L.; Stankovic, V.; Liao, J.; Wilson, C. Measuring the energy intensity of domestic activities from smart meter data. Appl. Energy 2016, 183, 1565–1580. [15] Sani, A.S., Yuan, D., Jin, J., Gao, L., Yu, S., Dong, Z.Y., 2019. Cyber security framework for Internet of things-based energy internet. Future Generat. Comput. Syst. 93, 849e859. https://doi.org/10.1016/j.future.2018.01.029. [16] Minoli, D., Sohraby, K., Occhiogrosso, B., 2017. IoT considerations, requirements, and architectures for smart buildings-energy optimization and next-generation building management systems. IEEE Internet Things J 4, 269e283. https:// doi.org/10.1109/JIOT.2017.2647881. [17] Yang,W.,Wang, J., Niu, T., Du, P., 2019c. A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting. Appl. Energy 235, 1205e1225. https://doi.org/10.1016/j.apenergy.2018.11.034 [18] Gielen, D., Boshell, F., Saygin, D., Bazilian, M.D.,Wagner, N., Gorini, R., 2019. The role of renewable energy in the global energy transformation. Energy Strateg. Rev. 24, 38e50. https://doi.org/10.1016/j.esr.2019.01.006. [19] 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. Renew. Sustain. Energy Rev. 109, 85e101. https://doi.org/10.1016/ j.rser.2019.04.021. [20] Guo, Y., Tan, Z., Chen, H., Li, G., Wang, J., Huang, R., Liu, J., Ahmad, T., 2018. Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving. Appl. Energy 225, 732e745. https://doi.org/10.1016/j.apenergy.2018.05.075 [21] Zhao, Y., Xiao, F., Wang, S., 2013. An intelligent chiller fault detection and diagnosis methodology using a Bayesian belief network. Energy Build. 57, 278e288. https://doi.org/10.1016/j.enbuild.2012.11.007. [22] Tang, Y., Huang, Y., Wang, H., Wang, C., Guo, qiang, Yao, W., 2018. Framework for artificial intelligence analysis in large-scale power grids based on digital simulation. CSEE J. Power Energy Syst. 4, 459e468. https://doi.org/10.17775/ cseejpes.2018.01010. [23] Das, R., Wang, Y., Putrus, G., Kotter, R., Marzband, M., Herteleer, B., Warmerdam, J., Multiobjective techno-economic-environmental optimisation of electric vehicle for energy services. Appl. Energy 257, https://doi.org/10.1016/j.apenergy.2019.113965. [24] 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. Renew. Energy 133, 620e635. https://doi.org/10.1016/j.renene.2018.10.047. [25] Engels, P.D.A., Kunkis, M., Altstaedt, S., 2020. A new energy world in the making: imaginary business futures in a dramatically changing world of decarbonized energy production. Energy Res. Soc. Sci. 60, 101321 https://doi.org/10.1016/j.erss.2019.101321. [26] Hadi Hojjati, Thi Kieu Khanh Ho, Narges Armanfard, Self-supervised anomaly detection in computer vision and beyond: A survey and outlook, Neural Networks, Volume 172, 2024, 106106, ISSN 0893-6080, https://doi.org/10.1016/j.neunet.2024.106106. [27] Chunying Zhang, Donghao Jia, Liya Wang, Wenjie Wang, Fengchun Liu, Aimin Yang, Comparative research on network intrusion detection methods based on machine learning, Computers & Security, Volume 121, 2022, 102861, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2022.102861. [28] Taye, Mohammad. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers. 12. 91. 10.3390/computers12050091. [29] Mamun, Abdullah & Sohel, Md & Sami, Naeem Md & Sunny, Md. Samiul & Roy, Debopriya & Hossain, Eklas. (2020). A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models. IEEE Access. PP. 1-1. 10.1109/ACCESS.2020.3010702. [30] Ajayi, Abiola Samuel, Sugyeong Kim, and Rin Yun (2024). "Study of developing a condensati on heat transfer coefficient and pressure drop model for whole reduced pressure ranges." International Journal of Air-Conditioning and Refrigeration 32, no. 1: 15. https://doi.org/10.1007/s44189-024-00060-0