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Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

1Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam

2Department of Computer Science & Engineering, Mepco Schlenk Engineering College, Sivakasi, Virudhunagar, Tamil Nadu, India

3Institute of Maritime, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam

4 Faculty of Automotive Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Viet Nam

5 Institute of Mechanical Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam

6 PATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam

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Received: 26 Dec 2023; Revised: 16 Jan 2024; Accepted: 10 Feb 2024; Available online: 21 Feb 2024; Published: 1 Mar 2024.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2024 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.  

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Keywords: Artificial intelligence; Machine learning; Energy forecasting; Artificial Neural Network; Energy management, Predictive Analytics, Energy sustainability

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  1. Abdalla, O., Rezk, H., Ahmed, E.M., (2019). Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance. Sol. Energy 180, 429–444. https://doi.org/10.1016/j.solener.2019.01.056
  2. Abdallah, M., Abu Talib, M., Feroz, S., Nasir, Q., Abdalla, H., Mahfood, B., (2020). Artificial intelligence applications in solid waste management: A systematic research review. Waste Manag. 109, 231–246. https://doi.org/10.1016/j.wasman.2020.04.057
  3. Abedinia, O., Amjady, N., Ghadimi, N., (2018). Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput. Intell. 34, 241–260. https://doi.org/10.1111/coin.12145
  4. Adhikari, D.R., Techato, K., Jariyaboon, R., (2024). A systematic literature review on renewable energy technologies for energy sustainability in Nepal: Key challenges and opportunities. Int. J. Renew. Energy Dev. 13, 206–222. https://doi.org/10.61435/ijred.2024.60032
  5. Ağbulut, Ü., (2022a). A novel stochastic model for very short-term wind speed forecasting in the determination of wind energy potential of a region: A case study from Turkey. Sustain. Energy Technol. Assessments 51, 101853. https://doi.org/10.1016/j.seta.2021.101853
  6. Ağbulut, Ü., (2022b). Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustain. Prod. Consum. 29, 141–157. https://doi.org/10.1016/j.spc.2021.10.001
  7. Ağbulut, Ü., Gürel, A.E., Biçen, Y., (2021). Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison. Renew. Sustain. Energy Rev. 135, 110114. https://doi.org/10.1016/j.rser.2020.110114
  8. Ağbulut, Ü., Gürel, A.E., Ergün, A., Ceylan, İ., (2020). Performance assessment of a V-trough photovoltaic system and prediction of power output with different machine learning algorithms. J. Clean. Prod. 268, 122269. https://doi.org/10.1016/j.jclepro.2020.122269
  9. Aguilar, J., Garces-Jimenez, A., R-Moreno, M.D., García, R., (2021). A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings. Renew. Sustain. Energy Rev. 151, 111530. https://doi.org/10.1016/j.rser.2021.111530
  10. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., Chen, H., (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. J. Clean. Prod. 289. https://doi.org/10.1016/j.jclepro.2021.125834
  11. Ahmed, W., Ansari, H., Khan, B., Ullah, Z., Ali, S.M., Mehmood, C.A.A., Qureshi, M.B., Hussain, I., Jawad, M., Khan, M.U.S., Ullah, A., Nawaz, R., (2020a). Machine learning based energy management model for smart grid and renewable energy districts. IEEE Access 8, 185059–185078. https://doi.org/10.1109/ACCESS.2020.3029943
  12. Ahmed, W., Ansari, H., Khan, B., Ullah, Z., Ali, S.M., Mehmood, C.A.A., Qureshi, M.B., Hussain, I., Jawad, M., Khan, M.U.S., Ullah, A., Nawaz, R., (2020b). Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts. IEEE Access 8, 185059–185078. https://doi.org/10.1109/ACCESS.2020.3029943
  13. Akhter, M.N., Mekhilef, S., Mokhlis, H., Shah, N.M., (2019). Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques. IET Renew. Power Gener. 13, 1009–1023. https://doi.org/10.1049/iet-rpg.2018.5649
  14. Al Sasongko, S.M., Jayanti, E.D., Ariessaputra, S., (2022). Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning. JOIV Int. J. Informatics Vis. 6, 835–841. https://doi.org/10.30630/joiv.6.4.1532
  15. Al Sumarmad, K.A., Sulaiman, N., Wahab, N.I.A., Hizam, H., (2022). Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers. Energies 15, 303. https://doi.org/10.3390/en15010303
  16. Albarakati, A.J., Boujoudar, Y., Azeroual, M., Jabeur, R., Aljarbouh, A., El Moussaoui, H., Lamhamdi, T., Ouaaline, N., (2021). Real-Time Energy Management for DC Microgrids Using Artificial Intelligence. Energies 14, 5307. https://doi.org/10.3390/en14175307
  17. Ali, A., Muhadi, Al Rasyid, M.U.H., Syarif, I., (2023). Development User Interface Hospital Management Information System Based on a Heuristic Evaluation Approach in Surabaya Hospital Medical Services. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 1456–1462. https://doi.org/10.18517/ijaseit.13.4.16529
  18. Alsafasfeh, Q., (2020). An Efficient Algorithm for Power Prediction in PV Generation System. Int. J. Renew. Energy Dev. 9, 207–216. https://doi.org/10.14710/ijred.9.2.207-216
  19. Anandika, A., Ferdian, R., Eriyandha, A., Suwandi, R., Hafidz, M., (2023). Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control. JOIV Int. J. Informatics Vis. 7, 2558–2568. https://doi.org/10.30630/joiv.7.4.01745
  20. Aniza, R., Chen, W.-H., Pétrissans, A., Hoang, A.T., Ashokkumar, V., Pétrissans, M., (2023). A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach. Environ. Pollut. 324, 121363. https://doi.org/10.1016/j.envpol.2023.121363
  21. Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., Wattam, S., (2020). Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renew. Sustain. Energy Rev. 130, 109899. https://doi.org/10.1016/j.rser.2020.109899
  22. Arabali, A., Ghofrani, M., Etezadi-Amoli, M., Fadali, M.S., Baghzouz, Y., (2013). Genetic-Algorithm-Based Optimization Approach for Energy Management. IEEE Trans. Power Deliv. 28, 162–170. https://doi.org/10.1109/TPWRD.2012.2219598
  23. Arcos-Aviles, D., Pascual, J., Marroyo, L., Sanchis, P., Guinjoan, F., (2018). Fuzzy logic-based energy management system design for residential grid-connected microgrids. IEEE Trans. Smart Grid 9, 530–543. https://doi.org/10.1109/TSG.2016.2555245
  24. Armin Razmjoo, A., Sumper, A., Davarpanah, A., (2019). Development of sustainable energy indexes by the utilization of new indicators: A comparative study. Energy Reports 5, 375–383. https://doi.org/10.1016/j.egyr.2019.03.006
  25. Atmaca, A., Atmaca, N., (2015). Life cycle energy (LCEA) and carbon dioxide emissions (LCCO2A) assessment of two residential buildings in Gaziantep, Turkey. Energy Build. 102, 417–431. https://doi.org/10.1016/j.enbuild.2015.06.008
  26. Aung, H.N., Khambadkone, A.M., Srinivasan, D., Logenthiran, T., (2010). Agent-based intelligent control for real-time operation of a microgrid, in: 2010 Joint International Conference on Power Electronics, Drives and Energy Systems & 2010 Power India. IEEE, pp. 1–6. https://doi.org/10.1109/PEDES.2010.5712495
  27. Aviles, D.A., Guinjoan, F., Barricarte, J., Marroyo, L., Sanchis, P., Valderrama, H., (2012). Battery management fuzzy control for a grid- tied microgrid with renewable generation, in: IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society. IEEE, pp. 5607–5612. https://doi.org/10.1109/IECON.2012.6389008
  28. Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M., (2020). Monarch Butterfly Optimization Based Convolutional Neural Network Design. Mathematics 8, 936. https://doi.org/10.3390/math8060936
  29. Bakay, M.S., Ağbulut, Ü., (2021). Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms. J. Clean. Prod. 285, 125324. https://doi.org/10.1016/j.jclepro.2020.125324
  30. Bakır, H., Ağbulut, Ü., Gürel, A.E., Yıldız, G., Güvenç, U., Soudagar, M.E.M., Hoang, A.T., Deepanraj, B., Saini, G., Afzal, A., (2022). Forecasting of future greenhouse gas emission trajectory for India using energy and economic indexes with various metaheuristic algorithms. J. Clean. Prod. 360, 131946. https://doi.org/10.1016/j.jclepro.2022.131946
  31. Barkah, A.S., Selamat, S.R., Abidin, Z.Z., Wahyudi, R., (2023). Impact of Data Balancing and Feature Selection on Machine Learning-based Network Intrusion Detection. JOIV Int. J. Informatics Vis. 7, 241–248. https://doi.org/10.30630/joiv.7.1.1041
  32. Barrett, E., Linder, S., (2015). Autonomous HVAC Control, A Reinforcement Learning Approach. pp. 3–19. https://doi.org/10.1007/978-3-319-23461-8_1
  33. Barricarte, J.J., Martín, I., Sanchis, P., Marroyo, L., (2011). Energy management strategies for grid integration of microgrids based on renewable energy sources, in: Int. Conf. Sustain. Energy Technol. pp. 4–7
  34. Bazenkov, N., Goubko, M., (2018). Advanced Planning of Home Appliances with Consumer’s Preference Learning. pp. 249–259. https://doi.org/10.1007/978-3-030-00617-4_23
  35. Bechouat, M., Younsi, A., Sedraoui, M., Soufi, Y., Yousfi, L., Tabet, I., Touafek, K., (2017). Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods. Int. J. Energy Environ. Eng. 8, 331–341. https://doi.org/10.1007/s40095-017-0252-6
  36. Bessa, R.J., Trindade, A., Silva, C.S.P., Miranda, V., (2015). Probabilistic solar power forecasting in smart grids using distributed information. Int. J. Electr. Power Energy Syst. 72, 16–23. https://doi.org/10.1016/j.ijepes.2015.02.006
  37. Bisri, A., Man, M., (2023). Machine Learning Algorithms Based on Sampling Techniques for Raisin Grains Classification. JOIV Int. J. Informatics Vis. 7, 7–14. https://doi.org/10.30630/joiv.7.1.970
  38. Biswas, P.P., Chen, W.-H., Lam, S.S., Park, Y.-K., Chang, J.-S., Hoang, A.T., (2023). A comprehensive study of artificial neural network for sensitivity analysis and hazardous elements sorption predictions via bone char for wastewater treatment. J. Hazard. Mater. 133154. https://doi.org/10.1016/j.jhazmat.2023.133154
  39. Bogaraj, T., Kanakaraj, J., (2016). Intelligent energy management control for independent microgrid. Sādhanā 41, 755–769. https://doi.org/10.1007/s12046-016-0515-6
  40. Boudoudouh, S., Maâroufi, M., (2018). Multi agent system solution to microgrid implementation. Sustain. Cities Soc. 39, 252–261. https://doi.org/10.1016/j.scs.2018.02.020
  41. Bruni, G., Cordiner, S., Mulone, V., Rocco, V., Spagnolo, F., (2015). A study on the energy management in domestic micro-grids based on Model Predictive Control strategies. Energy Convers. Manag. 102, 50–58. https://doi.org/10.1016/j.enconman.2015.01.067
  42. Bukar, A.L., Tan, C.W., Said, D.M., Dobi, A.M., Ayop, R., Alsharif, A., (2022). Energy management strategy and capacity planning of an autonomous microgrid: Performance comparison of metaheuristic optimization searching techniques. Renew. Energy Focus 40, 48–66. https://doi.org/10.1016/j.ref.2021.11.004
  43. Cao, B., Dong, W., Lv, Z., Gu, Y., Singh, S., Kumar, P., (2020a). Hybrid Microgrid Many-Objective Sizing Optimization With Fuzzy Decision. IEEE Trans. Fuzzy Syst. 28, 2702–2710. https://doi.org/10.1109/TFUZZ.2020.3026140
  44. Cao, B., Wang, X., Zhang, W., Song, H., Lv, Z., (2020b). A Many-Objective Optimization Model of Industrial Internet of Things Based on Private Blockchain. IEEE Netw. 34, 78–83. https://doi.org/10.1109/MNET.011.1900536
  45. Cao, B., Zhao, J., Gu, Y., Fan, S., Yang, P., (2020c). Security-Aware Industrial Wireless Sensor Network Deployment Optimization. IEEE Trans. Ind. Informatics 16, 5309–5316. https://doi.org/10.1109/TII.2019.2961340
  46. Cao, B., Zhao, J., Gu, Y., Ling, Y., Ma, X., (2020d). Applying graph-based differential grouping for multiobjective large-scale optimization. Swarm Evol. Comput. 53, 100626. https://doi.org/10.1016/j.swevo.2019.100626
  47. Cao, B., Zhao, J., Yang, P., Gu, Y., Muhammad, K., Rodrigues, J.J.P.C., de Albuquerque, V.H.C., (2020e). Multiobjective 3-D Topology Optimization of Next-Generation Wireless Data Center Network. IEEE Trans. Ind. Informatics 16, 3597–3605. https://doi.org/10.1109/TII.2019.2952565
  48. Cavalcante, L., Bessa, R.J., Reis, M., Browell, J., (2017). LASSO vector autoregression structures for very short-term wind power forecasting. Wind Energy 20, 657–675. https://doi.org/10.1002/we.2029
  49. Chandrasekaran, Y.J., Gunamony, S.L., Chandran, B.P., (2019). Integration of 5G Technologies in Smart Grid Communication-A Short Survey. Int. J. Renew. Energy Dev. 8, 275–283. https://doi.org/10.14710/ijred.8.3.275-283
  50. Chaoraingern, J., Tipsuwanporn, V., Numsomran, A., (2023). Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 2328–2337. https://doi.org/10.18517/ijaseit.13.6.18958
  51. Chau, M.Q., Nguyen, X.P., Huynh, T.T., Chu, V.D., Le, T.H., Nguyen, T.P., Nguyen, D.T., (2021). Prospects of application of IoT-based advanced technologies in remanufacturing process towards sustainable development and energy-efficient use. Energy Sources, Part A Recover. Util. Environ. Eff. 1–25. https://doi.org/10.1080/15567036.2021.1994057
  52. Chen, H., Qiao, H., Xu, L., Feng, Q., Cai, K., (2019). A fuzzy optimization strategy for the implementation of RBF LSSVR model in vis–NIR analysis of pomelo maturity. IEEE Trans. Ind. Informatics 15, 5971–5979
  53. Chen, Hao, Heidari, A.A., Chen, Huiling, Wang, M., Pan, Z., Gandomi, A.H., (2020). Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies. Futur. Gener. Comput. Syst. 111, 175–198. https://doi.org/10.1016/j.future.2020.04.008
  54. Chen, W.-H., Aniza, R., Arpia, A.A., Lo, H.-J., Hoang, A.T., Goodarzi, V., Gao, J., (2022a). A comparative analysis of biomass torrefaction severity index prediction from machine learning. Appl. Energy 324, 119689. https://doi.org/10.1016/j.apenergy.2022.119689
  55. Chen, W.-H., Wang, J.-S., Chang, M.-H., Mutuku, J.K., Hoang, A.T., (2021). Efficiency improvement of a vertical-axis wind turbine using a deflector optimized by Taguchi approach with modified additive method. Energy Convers. Manag. 245, 114609. https://doi.org/10.1016/j.enconman.2021.114609
  56. Chen, W.-H., Wang, J.-S., Chang, M.-H., Tuan Hoang, A., Shiung Lam, S., Kwon, E.E., Ashokkumar, V., (2022b). Optimization of a vertical axis wind turbine with a deflector under unsteady wind conditions via Taguchi and neural network applications. Energy Convers. Manag. 254, 115209. https://doi.org/10.1016/j.enconman.2022.115209
  57. Chen, Y., Norford, L.K., Samuelson, H.W., Malkawi, A., (2018). Optimal control of HVAC and window systems for natural ventilation through reinforcement learning. Energy Build. 169, 195–205. https://doi.org/10.1016/j.enbuild.2018.03.051
  58. Chen, Z., Liu, Y., Zhang, Y., Lei, Z., Chen, Zheng, Li, G., (2022). A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles. Energy 243, 122727. https://doi.org/10.1016/j.energy.2021.122727
  59. Chenxiao Guan, Wang, Y., Xue Lin, Nazarian, S., Pedram, M., (2015). Reinforcement learning-based control of residential energy storage systems for electric bill minimization, in: 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC). IEEE, pp. 637–642. https://doi.org/10.1109/CCNC.2015.7158054
  60. Chiñas-Palacios, C., Vargas-Salgado, C., Aguila-Leon, J., Hurtado-Pérez, E., (2021a). A cascade hybrid PSO feed-forward neural network model of a biomass gasification plant for covering the energy demand in an AC microgrid. Energy Convers. Manag. 232, 113896. https://doi.org/10.1016/j.enconman.2021.113896
  61. Chiñas-Palacios, C., Vargas-Salgado, C., Aguila-Leon, J., Hurtado-Pérez, E., (2021b). A cascade hybrid PSO feed-forward neural network model of a biomass gasification plant for covering the energy demand in an AC microgrid. Energy Convers. Manag. 232, 113896. https://doi.org/10.1016/j.enconman.2021.113896
  62. Chou, J.-S., Hsu, S.-C., Ngo, N.-T., Lin, C.-W., Tsui, C.-C., (2019a). Hybrid Machine Learning System to Forecast Electricity Consumption of Smart Grid-Based Air Conditioners. IEEE Syst. J. 13, 3120–3128. https://doi.org/10.1109/JSYST.2018.2890524
  63. Chou, J.-S., Hsu, S.-C., Ngo, N.-T., Lin, C.-W., Tsui, C.-C., (2019b). Hybrid Machine Learning System to Forecast Electricity Consumption of Smart Grid-Based Air Conditioners. IEEE Syst. J. 13, 3120–3128. https://doi.org/10.1109/JSYST.2018.2890524
  64. Corizzo, R., Ceci, M., Fanaee-T, H., Gama, J., (2021). Multi-aspect renewable energy forecasting. Inf. Sci. (Ny). 546, 701–722. https://doi.org/10.1016/j.ins.2020.08.003
  65. Dai, P., Yu, W., Wen, G., Baldi, S., (2020). Distributed Reinforcement Learning Algorithm for Dynamic Economic Dispatch With Unknown Generation Cost Functions. IEEE Trans. Ind. Informatics 16, 2258–2267. https://doi.org/10.1109/TII.2019.2933443
  66. de Ville, B., (2013). Decision trees. Wiley Interdiscip. Rev. Comput. Stat. 5, 448–455. https://doi.org/10.1002/wics.1278
  67. Desale, S., Rasool, A., Andhale, S., Rane, P., (2015). Heuristic and meta-heuristic algorithms and their relevance to the real world: a survey. Int. J. Comput. Eng. Res. Trends 351, 2349–7084
  68. Dobbelaere, M.R., Plehiers, P.P., Van de Vijver, R., Stevens, C. V., Van Geem, K.M., (2021). Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats. Engineering 7, 1201–1211. https://doi.org/10.1016/j.eng.2021.03.019
  69. Domachowski, Z., (2021). Minimizing Greenhouse Gas Emissions From Ships Using a Pareto Multi-Objective Optimization Approach. Polish Marit. Res. 28, 96–101. https://doi.org/10.2478/pomr-2021-0026
  70. Dong, V.H., Sharma, P., (2023). Optimized conversion of waste vegetable oil to biofuel with Meta heuristic methods and design of experiments. J. Emerg. Sci. Eng. 1, 22–28. https://doi.org/10.61435/jese.2023.4
  71. Dong, W., Yang, Q., Fang, X., Ruan, W., (2021). Adaptive optimal fuzzy logic based energy management in multi-energy microgrid considering operational uncertainties. Appl. Soft Comput. 98, 106882. https://doi.org/10.1016/j.asoc.2020.106882
  72. Doukas, H., Patlitzianas, K.D., Iatropoulos, K., Psarras, J., (2007). Intelligent building energy management system using rule sets. Build. Environ. 42, 3562–3569. https://doi.org/10.1016/j.buildenv.2006.10.024
  73. Drzewiecki, M., Guziński, J., (2023). Design of an Autonomous IoT Node Powered by a Perovskite-Based Wave Energy Converter. Polish Marit. Res. 30, 142–152. https://doi.org/10.2478/pomr-2023-0047
  74. Duan, Jikai, Zuo, H., Bai, Y., Duan, Jizheng, Chang, M., Chen, B., (2021). Short-term wind speed forecasting using recurrent neural networks with error correction. Energy 217, 119397. https://doi.org/10.1016/j.energy.2020.119397
  75. Duraković, B., Halilovic, M., (2023). Industry 4.0: The New Quality Management Paradigm in Era of Industrial Internet of Things. JOIV Int. J. Informatics Vis. 7, 580–587. https://doi.org/10.30630/joiv.7.2.1738
  76. El-Shafay, A.S., Gad, M.S., Ağbulut, Ü., Attia, E.-A., (2023). Optimization of performance and emission outputs of a CI engine powered with waste fat biodiesel: A detailed RSM, fuzzy multi-objective and MCDM application. Energy 275, 127356. https://doi.org/10.1016/j.energy.2023.127356
  77. Elsheikh, A.H., Sharshir, S.W., Abd Elaziz, M., Kabeel, A.E., Guilan, W., Haiou, Z., (2019). Modeling of solar energy systems using artificial neural network: A comprehensive review. Sol. Energy 180, 622–639. https://doi.org/10.1016/j.solener.2019.01.037
  78. Erfianto, B., Rahmatsyah, A., (2022). Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation. JOIV Int. J. Informatics Vis. 6, 771–777. https://doi.org/10.30630/joiv.6.4.889
  79. Eseye, A.T., Zhang, J., Zheng, D., (2018). Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information. Renew. Energy 118, 357–367. https://doi.org/10.1016/J.RENENE.2017.11.011
  80. Faia, R., Pinto, T., Vale, Z., Corchado, J., (2017). An Ad-Hoc Initial Solution Heuristic for Metaheuristic Optimization of Energy Market Participation Portfolios. Energies 10, 883. https://doi.org/10.3390/en10070883
  81. Förderer, K., Ahrens, M., Bao, K., Mauser, I., Schmeck, H., (2018). Towards the Modeling of Flexibility Using Artificial Neural Networks in Energy Management and Smart Grids, in: Proceedings of the Ninth International Conference on Future Energy Systems. ACM, New York, NY, USA, pp. 85–90. https://doi.org/10.1145/3208903.3208915
  82. Fossati, J.P., Galarza, A., Martín-Villate, A., Echeverría, J.M., Fontán, L., (2015). Optimal scheduling of a microgrid with a fuzzy logic controlled storage system. Int. J. Electr. Power Energy Syst. 68, 61–70. https://doi.org/10.1016/j.ijepes.2014.12.032
  83. Fu, X., Pace, P., Aloi, G., Yang, L., Fortino, G., (2020). Topology optimization against cascading failures on wireless sensor networks using a memetic algorithm. Comput. Networks 177, 107327. https://doi.org/10.1016/j.comnet.2020.107327
  84. Fuadi, A.Z., Haq, I.N., Leksono, E., (2021). Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory
  85. Furman, J., Seamans, R., (2019). AI and the Economy. Innov. Policy Econ. 19, 161–191. https://doi.org/10.1086/699936
  86. Galván, I.M., Valls, J.M., Cervantes, A., Aler, R., (2017). Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks. Inf. Sci. (Ny). 418–419, 363–382. https://doi.org/10.1016/j.ins.2017.08.039
  87. Gangolells, M., Casals, M., Forcada, N., Macarulla, M., Giretti, A., (2016). Energy performance assessment of an intelligent energy management system. Renew. Sustain. Energy Rev. 55, 662–667. https://doi.org/10.1016/j.rser.2015.11.006
  88. Gao, J.B., Gunn, S.R., Harris, C.J., (2003). SVM regression through variational methods and its sequential implementation. Neurocomputing 55, 151–167. https://doi.org/10.1016/S0925-2312(03)00365-5
  89. Goswami, P., Mukherjee, A., Hazra, R., Yang, L., Ghosh, U., Qi, Y., Wang, H., (2022). AI Based Energy Efficient Routing Protocol for Intelligent Transportation System. IEEE Trans. Intell. Transp. Syst. 23, 1670–1679. https://doi.org/10.1109/TITS.2021.3107527
  90. Goyal, D., Goyal, T., Mahla, S.K., Goga, G., Dhir, A., Balasubramanian, D., Hoang, A.T., Wae-Hayee, M., Josephin, J.S.F., Sonthalia, A., Varuvel, E.G., Brindhadevi, K., (2023). Application of Taguchi design in optimization of performance and emissions characteristics of n-butanol/diesel/biogas under dual fuel mode. Fuel 338, 127246. https://doi.org/10.1016/j.fuel.2022.127246
  91. Halabi, L.M., Mekhilef, S., Hossain, M., (2018). Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation. Appl. Energy 213, 247–261. https://doi.org/10.1016/j.apenergy.2018.01.035
  92. Haleem, A., Javaid, M., Khan, I.H., (2019). Current status and applications of Artificial Intelligence (AI) in medical field: An overview. Curr. Med. Res. Pract. 9, 231–237. https://doi.org/10.1016/j.cmrp.2019.11.005
  93. Han, L., Jiao, X., Zhang, Z., (2020). Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging. Energies 13, 202. https://doi.org/10.3390/en13010202
  94. Han, T., Wang, Z., Meng, H., (2022). End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation. J. Power Sources 520, 230823. https://doi.org/10.1016/j.jpowsour.2021.230823
  95. Haque, R., Quek, A., Ting, C., Goh, H., Hasan, R., (2024). Classification Techniques Using Machine Learning for Graduate Student Employability Predictions. Int. J. Adv. Sci. Eng. Inf. Technol. 14, 45–56
  96. Hasnaoui, A., Omari, A., Azzouz, Z. eddine, Danoune, M.B., Arini, N.R., (2023). Reduction of Electricity Cost of Residential Home Using PSO and WOA Optimization Method. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 828–834. https://doi.org/10.18517/ijaseit.13.3.18374
  97. Hoang, A.T., Nguyen, X.P., Le, A.T., Huynh, T.T., Pham, V.V., (2021). COVID-19 and the Global Shift Progress to Clean Energy. J. Energy Resour. Technol. 143. https://doi.org/10.1115/1.4050779
  98. Hoang, A.T., Pandey, A., Lichtfouse, E., Bui, V.G., Veza, I., Nguyen, H.L., Nguyen, X.P., (2023a). Green hydrogen economy: Prospects and policies in Vietnam. Int. J. Hydrogen Energy 48, 31049–31062. https://doi.org/10.1016/j.ijhydene.2023.05.306
  99. Hoang, A.T., Pandey, A., Martinez De Osés, F.J., Chen, W.-H., Said, Z., Ng, K.H., Ağbulut, Ü., Tarełko, W., Ölçer, A.I., Nguyen, X.P., (2023b). Technological solutions for boosting hydrogen role in decarbonization strategies and net-zero goals of world shipping: Challenges and perspectives. Renew. Sustain. Energy Rev. 188, 113790. https://doi.org/10.1016/j.rser.2023.113790
  100. Horný, M., (2014). Bayesian networks. Bost. Univ. Sch. Public Heal. 17
  101. Houssein, E.H., (2019). Machine Learning and Meta-heuristic Algorithms for Renewable Energy: A Systematic Review. pp. 165–187. https://doi.org/10.1007/978-981-13-5995-8_7
  102. Hu, B., (2018). Application of Evaluation Algorithm for Port Logistics Park Based on Pca-Svm Model. Polish Marit. Res. 25, 29–35. https://doi.org/10.2478/pomr-2018-0109
  103. Hu, J., Chen, H., Heidari, A.A., Wang, M., Zhang, X., Chen, Y., Pan, Z., (2021). Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection. Knowledge-Based Syst. 213, 106684. https://doi.org/10.1016/j.knosys.2020.106684
  104. Hu, Z., Qin, W., (2017). Fuzzy Method and Neural Network Model Parallel Implementation of Multi-Layer Neural Network Based on Cloud Computing for Real Time Data Transmission in Large Offshore Platform. Polish Marit. Res. 24, 39–44. https://doi.org/10.1515/pomr-2017-0062
  105. Ihsan, A.F., Darmadi, Uttunggadewa, S., Rahmawati, S.D., Giovanni, I., Himawan, S.N., (2023). Performance Analysis of Deep Learning Implementation in Operational Condition Forecasting of a Gas Transmission Pipeline Network. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 1423–1429. https://doi.org/10.18517/ijaseit.13.4.18250
  106. Ikeda, S., Nagai, T., (2021). A novel optimization method combining metaheuristics and machine learning for daily optimal operations in building energy and storage systems. Appl. Energy 289, 116716. https://doi.org/10.1016/j.apenergy.2021.116716
  107. Ilham, N.I., Hussin, M.Z., Dahlan, N.Y., Setiawan, E.A., (2022). Prospects and Challenges of Malaysia’s Distributed Energy Resources in Business Models Towards Zero – Carbon Emission and Energy Security. Int. J. Renew. Energy Dev. 11, 1089–1100. https://doi.org/10.14710/ijred.2022.45662
  108. Issa, E., Al-Gazzar, M., Seif, M., (2022). Energy management of Renewable Energy Sources based on Support Vector Machine. Int. J. Renew. Energy Res. 12. https://doi.org/10.20508/ijrer.v12i2.12897.g8461
  109. Jadhav, A.M., Patne, N.R., (2017). Priority-Based Energy Scheduling in a Smart Distributed Network With Multiple Microgrids. IEEE Trans. Ind. Informatics 13, 3134–3143. https://doi.org/10.1109/TII.2017.2671923
  110. Jain, D.K., Neelakandan, S., Veeramani, T., Bhatia, S., Memon, F.H., (2022). Design of fuzzy logic based energy management and traffic predictive model for cyber physical systems. Comput. Electr. Eng. 102, 108135. https://doi.org/10.1016/j.compeleceng.2022.108135
  111. Jawad, M., Qureshi, M.B., Khan, M.U.S., Ali, S.M., Mehmood, A., Khan, B., Wang, X., Khan, S.U., (2021). A Robust Optimization Technique for Energy Cost Minimization of Cloud Data Centers. IEEE Trans. Cloud Comput. 9, 447–460. https://doi.org/10.1109/TCC.2018.2879948
  112. Jha, S.K., Bilalovic, J., Jha, A., Patel, N., Zhang, H., (2017). Renewable energy: Present research and future scope of Artificial Intelligence. Renew. Sustain. Energy Rev. 77, 297–317. https://doi.org/10.1016/j.rser.2017.04.018
  113. Jiang, B., Fei, Y., (2015). Smart Home in Smart Microgrid: A Cost-Effective Energy Ecosystem With Intelligent Hierarchical Agents. IEEE Trans. Smart Grid 6, 3–13. https://doi.org/10.1109/TSG.2014.2347043
  114. Jiang, P., Li, R., Lu, H., Zhang, X., (2020). Modeling of electricity demand forecast for power system. Neural Comput. Appl. 32, 6857–6875. https://doi.org/10.1007/s00521-019-04153-5
  115. Jo, T., (2021). Machine Learning Foundations. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-65900-4
  116. Karaağaç, M.O., Ergün, A., Ağbulut, Ü., Gürel, A.E., Ceylan, İ., (2021). Experimental analysis of CPV/T solar dryer with nano-enhanced PCM and prediction of drying parameters using ANN and SVM algorithms. Sol. Energy 218, 57–67. https://doi.org/10.1016/j.solener.2021.02.028
  117. Kaytez, F., Taplamacioglu, M.C., Cam, E., Hardalac, F., (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. Int. J. Electr. Power Energy Syst. 67, 431–438. https://doi.org/10.1016/j.ijepes.2014.12.036
  118. Khan, P.W., Byun, Y.-C., Lee, S.-J., Kang, D.-H., Kang, J.-Y., Park, H.-S., (2020). Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources. Energies 13, 4870. https://doi.org/10.3390/en13184870
  119. Kian, A.Y., Lim, S.C., (2023). On the Potential of Solar Energy for Chemical and Metal Manufacturing Plants in Malaysia. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 1898–1904. https://doi.org/10.18517/ijaseit.13.5.19052
  120. Kim, J., Moon, J., Hwang, E., Kang, P., (2019). Recurrent inception convolution neural network for multi short-term load forecasting. Energy Build. 194, 328–341. https://doi.org/10.1016/j.enbuild.2019.04.034
  121. Kim, M., Choi, W., Jeon, Y., Liu, L., (2019). A Hybrid Neural Network Model for Power Demand Forecasting. Energies 12, 931. https://doi.org/10.3390/en12050931
  122. Kim, S., (2024). Development of a TPACK Educational Program to Enhance Pre-service Teachers ’ Teaching Expertise in Artificial Intelligence Convergence Education. Int. J. Adv. Sci. Eng. Inf. Technol. 14, 1–9
  123. Kim, S.W., Go, H.N., Hong, S.J., Lee, Y., (2023). An Approach to the Utilization of Design Thinking in Artificial Intelligence Education. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 2198–2204. https://doi.org/10.18517/ijaseit.13.6.19042
  124. Kommuri, N.K., McGordon, A., Allen, A., Quang Truong, D., (2020). Evaluation of a Modified Equivalent Fuel-Consumption Minimization Strategy Considering Engine Start Frequency and Battery Parameters for a Plugin Hybrid Two-Wheeler. Energies 13, 3122. https://doi.org/10.3390/en13123122
  125. Kucęba, R., Zawada, M., Szajt, M., Kowalik, J., (2018). Prosumer energy as a stimulator of micro- Smart Grids development - on the consumer side. IOP Conf. Ser. Earth Environ. Sci. 164, 012003. https://doi.org/10.1088/1755-1315/164/1/012003
  126. Kumar, K., Thakur, G.S.M., (2012). Advanced Applications of Neural Networks and Artificial Intelligence: A Review. Int. J. Inf. Technol. Comput. Sci. 4, 57–68. https://doi.org/10.5815/ijitcs.2012.06.08
  127. Kusnawi, K., Rahardi, M., Pandiangan, V.D., (2023). Sentiment Analysis of Neobank Digital Banking using Support Vector Machine Algorithm in Indonesia. JOIV Int. J. Informatics Vis. 7, 377–383. https://doi.org/10.30630/joiv.7.2.1652
  128. Kuznetsova, E., Li, Y.-F., Ruiz, C., Zio, E., Ault, G., Bell, K., (2013). Reinforcement learning for microgrid energy management. Energy 59, 133–146. https://doi.org/10.1016/j.energy.2013.05.060
  129. Lagorse, J., Simoes, M.G., Miraoui, A., (2009). A multiagent fuzzy-logic-based energy management of hybrid systems. IEEE Trans. Ind. Appl. 45, 2123–2129. https://doi.org/10.1109/TIA.2009.2031786
  130. Lagouir, M., Badri, A., Sayouti, Y., (2021). Multi-Objective Optimization Dispatch Based Energy Management of A Microgrid Running Under Grid Connected and Standalone Operation Mode. Int. J. Renew. Energy Dev. 10, 333–343. https://doi.org/10.14710/ijred.2021.34656
  131. Lahlou, Y., Hajji, A., Aggour, M., (2023). Optimization of a Management Algorithm for an Innovative System of Automatic Switching between Two Photovoltaic and Wind Turbine Modes for an Ecological Production of Green Energy. Int. J. Renew. Energy Dev. 12, 36–45. https://doi.org/10.14710/ijred.2023.47137
  132. Le, A.T., Pandey, A., Sirohi, R., Sharma, P., Chen, W.-H., Pham, N.D.K., Tran, V.D., Nguyen, X.P., Hoang, A.T., (2023). Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations. Energy & Fuels 37, 17310–17327. https://doi.org/10.1021/acs.energyfuels.3c02868
  133. Lee, J., Davari, H., Singh, J., Pandhare, V., (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manuf. Lett. 18, 20–23. https://doi.org/10.1016/j.mfglet.2018.09.002
  134. Li, D., Jayaweera, S.K., (2015). Machine-Learning Aided Optimal Customer Decisions for an Interactive Smart Grid. IEEE Syst. J. 9, 1529–1540. https://doi.org/10.1109/JSYST.2014.2334637
  135. Li, J., Herdem, M.S., Nathwani, J., Wen, J.Z., (2023). Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management. Energy AI 11, 100208. https://doi.org/10.1016/j.egyai.2022.100208
  136. Li, X., Zang, C., Liu, W., Zeng, P., Yu, H., (2012). Metropolis Criterion Based Fuzzy Q-Learning Energy Management for Smart Grids. TELKOMNIKA Indones. J. Electr. Eng. 10. https://doi.org/10.11591/telkomnika.v10i8.1626
  137. Lin, H., Sun, K., Tan, Z., Liu, C., Guerrero, J.M., Vasquez, J.C., (2019). Adaptive protection combined with machine learning for microgrids. IET Gener. Transm. Distrib. 13, 770–779. https://doi.org/10.1049/iet-gtd.2018.6230
  138. Liu, B., Kang, J., Jiang, N., Jing, Y., (2011). Cost Control of the Transmission Congestion Management in Electricity Systems Based on Ant Colony Algorithm. Energy Power Eng. 03, 17–23. https://doi.org/10.4236/epe.2011.31003
  139. Liu, H., Chen, C., Lv, X., Wu, X., Liu, M., (2019). Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Convers. Manag. 195, 328–345. https://doi.org/10.1016/j.enconman.2019.05.020
  140. Liu, H., Mi, X., Li, Y., (2018). Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Convers. Manag. 156, 498–514. https://doi.org/10.1016/j.enconman.2017.11.053
  141. Liu, J., Wang, X., Lu, Y., (2017). A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system. Renew. Energy 103, 620–629. https://doi.org/10.1016/j.renene.2016.10.074
  142. Liu, R., Gupta, S., Patel, P., (2023). The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health. Inf. Syst. Front. 25, 2275–2299. https://doi.org/10.1007/s10796-021-10191-z
  143. Liu, S., Henze, G.P., (2006). Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory. Energy Build. 38, 148–161. https://doi.org/10.1016/j.enbuild.2005.06.001
  144. Liu, T., Liu, S., Heng, J., Gao, Y., (2018). A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm. Appl. Sci. 8, 1754. https://doi.org/10.3390/app8101754
  145. Liu, W., Zhuang, P., Liang, H., Peng, J., Huang, Z., (2018). Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning. IEEE Trans. Neural Networks Learn. Syst. 29, 2192–2203. https://doi.org/10.1109/TNNLS.2018.2801880
  146. Logenthiran, T., Srinivasan, D., Khambadkone, A.M., Aung, H.N., (2012). Multiagent System for Real-Time Operation of a Microgrid in Real-Time Digital Simulator. IEEE Trans. Smart Grid 3, 925–933. https://doi.org/10.1109/TSG.2012.2189028
  147. Lopes, J.A.P., Hatziargyriou, N., Mutale, J., Djapic, P., Jenkins, N., (2007). Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities. Electr. Power Syst. Res. 77, 1189–1203. https://doi.org/10.1016/j.epsr.2006.08.016
  148. Louzazni, M., Khouya, A., Amechnoue, K., Gandelli, A., Mussetta, M., Crăciunescu, A., (2018). Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm. Appl. Sci. 8, 339. https://doi.org/10.3390/app8030339
  149. Luger, G.F., (2005). Artificial intelligence: structures and strategies for complex problem solving. Pearson education
  150. Macarulla, M., Casals, M., Forcada, N., Gangolells, M., (2017). Implementation of predictive control in a commercial building energy management system using neural networks. Energy Build. 151, 511–519. https://doi.org/10.1016/j.enbuild.2017.06.027
  151. Mason, K., Grijalva, S., (2019). A review of reinforcement learning for autonomous building energy management. Comput. Electr. Eng. 78, 300–312. https://doi.org/10.1016/j.compeleceng.2019.07.019
  152. MathWorks, n.d. Deep Learning Toolbox Design, train, and analyze deep learning networks [WWW Document]
  153. Meenal, R., Selvakumar, A.I., (2018). Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters. Renew. Energy 121, 324–343. https://doi.org/10.1016/J.RENENE.2017.12.005
  154. Melinda, M., Arnia, F., Yafi, A., Afny, N., Andryani, C., Enriko, I.K.A., (2024). Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder. Int. J. Adv. Sci. Eng. Inf. Technol. 14, 57–64
  155. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D., (2015). Human-level control through deep reinforcement learning. Nature 518, 529–533. https://doi.org/10.1038/nature14236
  156. Moayedi, H., Mehrabi, M., Mosallanezhad, M., Rashid, A.S.A., Pradhan, B., (2019). Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Eng. Comput. 35, 967–984. https://doi.org/10.1007/s00366-018-0644-0
  157. Moayedi, H., Mosavi, A., (2021). An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework. Energies 14, 1196. https://doi.org/10.3390/en14041196
  158. Mocanu, E., Mocanu, D.C., Nguyen, P.H., Liotta, A., Webber, M.E., Gibescu, M., Slootweg, J.G., (2019). On-Line Building Energy Optimization Using Deep Reinforcement Learning. IEEE Trans. Smart Grid 10, 3698–3708. https://doi.org/10.1109/TSG.2018.2834219
  159. Mohamed, A., Mohammed, O., (2013). Real-time energy management scheme for hybrid renewable energy systems in smart grid applications. Electr. Power Syst. Res. 96, 133–143. https://doi.org/10.1016/j.epsr.2012.10.015
  160. Molderink, A., Bakker, V., Bosman, M.G.C., Hurink, J.L., Smit, G.J.M., (2009). Domestic energy management methodology for optimizing efficiency in Smart Grids, in: 2009 IEEE Bucharest PowerTech. IEEE, pp. 1–7. https://doi.org/10.1109/PTC.2009.5281849
  161. Montazeri-Gh, M., Pourbafarani, Z., (2017). Near-Optimal SOC Trajectory for Traffic-Based Adaptive PHEV Control Strategy. IEEE Trans. Veh. Technol. 66, 9753–9760. https://doi.org/10.1109/TVT.2017.2757604
  162. Mosavi, A., Salimi, M., Ardabili, S.F., Rabczuk, T., Shamshirband, S., Varkonyi-Koczy, A.R., (2019). State of the art of machine learning models in energy systems, a systematic review. Energies 12, 1301. https://doi.org/10.3390/en12071301
  163. Moura, M. das C., Zio, E., Lins, I.D., Droguett, E., (2011). Failure and reliability prediction by support vector machines regression of time series data. Reliab. Eng. Syst. Saf. 96, 1527–1534. https://doi.org/10.1016/j.ress.2011.06.006
  164. Natsheh, E.M., (2013). Hybrid Power Systems Energy Management Based on Artificial Intelligence. PHD Thesis Manchester Metrop. Univ
  165. Nguyen, H.P., Le, P.Q.H., Pham, V.V., Nguyen, X.P., Balasubramaniam, D., Hoang, A.-T., (2021). Application of the Internet of Things in 3E (efficiency, economy, and environment) factor-based energy management as smart and sustainable strategy. Energy Sources, Part A Recover. Util. Environ. Eff. 1–23. https://doi.org/10.1080/15567036.2021.1954110
  166. Nguyen, H.P., Nguyen, P.Q.P., Nguyen, D.K.P., Bui, V.D., Nguyen, D.T., (2023). Application of IoT Technologies in Seaport Management. JOIV Int. J. Informatics Vis. 7, 228–240. https://doi.org/10.30630/joiv.7.1.1697
  167. Nguyen, T.T.M., Khoa, P.N.D., Huynh, N.A., (2022). Electrical Energy Management According to Pricing Policy: A Case in Vietnam. Int. J. Renew. Energy Dev. 11, 851–862. https://doi.org/10.14710/ijred.2022.46302
  168. Nguyen, V.G., Rajamohan, S., Rudzki, K., Kozak, J., Sharma, P., Pham, N.D.K., Nguyen, P.Q.P., Xuan, P.N., (2023). Using Artificial Neural Networks for Predicting Ship Fuel Consumption. Polish Marit. Res. 30, 39–60. https://doi.org/10.2478/pomr-2023-0020
  169. Nguyen, V.N., Tarełko, W., Sharma, P., El-Shafay, A.S., Chen, W.-H., Nguyen, P.Q.P., Nguyen, X.P., Hoang, A.T., (2024). Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects. Energy & Fuels 38, 1692–1712. https://doi.org/10.1021/acs.energyfuels.3c04343
  170. Nguyen, X.P., Le, N.D., Pham, V.V., Huynh, T.T., Dong, V.H., Hoang, A.T., (2021). Mission, challenges, and prospects of renewable energy development in Vietnam. Energy Sources, Part A Recover. Util. Environ. Eff. 1–13. https://doi.org/10.1080/15567036.2021.1965264
  171. Nižetić, S., Arıcı, M., Hoang, A.T., (2023). Smart and Sustainable Technologies in Energy Transition. J. Clean. Prod. 135944. https://doi.org/10.1016/j.jclepro.2023.135944
  172. Novitasari, D.C.R., Fatmawati, F., Hendradi, R., Nariswari, R., Saputra, R.A., (2023). Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data. JOIV Int. J. Informatics Vis. 7, 2223–2229. https://doi.org/10.30630/joiv.7.4.01501
  173. Oğcu, G., Demirel, O.F., Zaim, S., (2012). Forecasting Electricity Consumption with Neural Networks and Support Vector Regression. Procedia - Soc. Behav. Sci. 58, 1576–1585. https://doi.org/10.1016/j.sbspro.2012.09.1144
  174. Panait, L., Luke, S., (2005). Cooperative Multi-Agent Learning: The State of the Art. Auton. Agent. Multi. Agent. Syst. 11, 387–434. https://doi.org/10.1007/s10458-005-2631-2
  175. Pang, W.L., Chung, G.C., Chan, K.Y., Ee, L.I., Roslee, M., Fitrey, E., Sim, Y.W., Prasetio, M.D., (2023). Smart Machine Learning-based IoT Health and Cough Monitoring System. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 1645–1653. https://doi.org/10.18517/ijaseit.13.5.19024
  176. Park, S., Lee, J., Bae, S., Hwang, G., Choi, J.K., (2016). Contribution-Based Energy-Trading Mechanism in Microgrids for Future Smart Grid: A Game Theoretic Approach. IEEE Trans. Ind. Electron. 63, 4255–4265. https://doi.org/10.1109/TIE.2016.2532842
  177. Pascual, J., Sanchis, P., Marroyo, L., (2014). Implementation and Control of a Residential Electrothermal Microgrid Based on Renewable Energies, a Hybrid Storage System and Demand Side Management. Energies 7, 210–237. https://doi.org/10.3390/en7010210
  178. Passino, K.M., Yurkovich, S., Reinfrank, M., (1998). Fuzzy control. Addison-wesley Reading, MA
  179. Paudel, S., Elmitri, M., Couturier, S., Nguyen, P.H., Kamphuis, R., Lacarrière, B., Le Corre, O., (2017). A relevant data selection method for energy consumption prediction of low energy building based on support vector machine. Energy Build. 138, 240–256. https://doi.org/10.1016/j.enbuild.2016.11.009
  180. Pérez-Lombard, L., Ortiz, J., Pout, C., (2008). A review on buildings energy consumption information. Energy Build. 40, 394–398. https://doi.org/10.1016/j.enbuild.2007.03.007
  181. Pollet, B.G., Lamb, J.J., (2020). Hydrogen, Biomass and Bioenergy: Integration Pathways for Renewable Energy Applications. Academic Press
  182. Prabha, V.L., Monie, E.C., (2007). Hardware Architecture of Reinforcement Learning Scheme for Dynamic Power Management in Embedded Systems. EURASIP J. Embed. Syst. 2007, 1–6. https://doi.org/10.1155/2007/65478
  183. Praseeda, K.I., Reddy, B.V.V., Mani, M., (2016). Embodied and operational energy of urban residential buildings in India. Energy Build. 110, 211–219. https://doi.org/10.1016/j.enbuild.2015.09.072
  184. Prodan, I., Zio, E., (2014). A model predictive control framework for reliable microgrid energy management. Int. J. Electr. Power Energy Syst. 61, 399–409. https://doi.org/10.1016/j.ijepes.2014.03.017
  185. Qu, S., Han, Y., Wu, Z., Raza, H., (2021). Consensus modeling with asymmetric cost based on data-driven robust optimization. Gr. Decis. Negot. 30, 1395–1432
  186. Quej, V.H., Almorox, J., Arnaldo, J.A., Saito, L., (2017). ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. J. Atmos. Solar-Terrestrial Phys. 155, 62–70. https://doi.org/10.1016/j.jastp.2017.02.002
  187. Radonjić, A., Pjevčević, D., Maraš, V., (2020). Neural Network Ensemble Approach to Pushed Convoys Dispatching Problems. Polish Marit. Res. 27, 70–82. https://doi.org/10.2478/pomr-2020-0008
  188. Ramesh, T., Prakash, R., Shukla, K.K., (2010). Life cycle energy analysis of buildings: An overview. Energy Build. 42, 1592–1600. https://doi.org/10.1016/j.enbuild.2010.05.007
  189. Ramirez-Sanchez, E., Evangelista-Palma, G., Gutierrez-Navarro, D., Kammen, D.M., Castellanos, S., (2022). Impacts and savings of energy efficiency measures: A case for Mexico’s electrical grid. J. Clean. Prod. 340, 130826. https://doi.org/10.1016/j.jclepro.2022.130826
  190. Rangkuti, A.H., Athalaa, V.H., Indallah, F.H., Febriansyah, F.F., (2023). Optimizing Hand Gesture Recognition Using CNN Model Supported by Raspberry pi for Self-Service Technology. JOIV Int. J. Informatics Vis. 7, 58–69. https://doi.org/10.30630/joiv.7.1.1032
  191. Rayati, M., Sheikhi, A., Ranjbar, A.M., (2015). Optimising operational cost of a smart energy hub, the reinforcement learning approach. Int. J. Parallel, Emergent Distrib. Syst. 30, 325–341. https://doi.org/10.1080/17445760.2014.974600
  192. Razak Kaladgi, A., Afzal, A., Manokar, A.M., Thakur, D., Agbulut, U., Alshahrani, S., Saleel C, A., Subbiah, R., (2021). Integrated Taguchi-GRA-RSM optimization and ANN modelling of thermal performance of zinc oxide nanofluids in an automobile radiator. Case Stud. Therm. Eng. 26, 101068. https://doi.org/10.1016/j.csite.2021.101068
  193. Remani, T., Jasmin, E.A., Ahamed, T.P.I., (2019). Residential Load Scheduling With Renewable Generation in the Smart Grid: A Reinforcement Learning Approach. IEEE Syst. J. 13, 3283–3294. https://doi.org/10.1109/JSYST.2018.2855689
  194. Reymond, M., Patyn, C., Rădulescu, R., Deconinck, G., Nowé, A., (2018). Reinforcement learning for demand response of domestic household appliances, in: Proceedings of the Adaptive and Learning Agents Workshop at FAIM
  195. Roiné, L., Therani, K., Manjili, Y.S., Jamshidi, M., (2014). Microgrid energy management system using fuzzy logic control, in: 2014 World Automation Congress (WAC). IEEE, pp. 462–467
  196. Roje, T., Marín, L.G., Sáez, D., Orchard, M., Jiménez-Estévez, G., (2017). Consumption modeling based on Markov chains and Bayesian networks for a demand side management design of isolated microgrids. Int. J. Energy Res. 41, 365–376. https://doi.org/10.1002/er.3607
  197. Rowlands, I.H., WISE, Chai, D.S., Wen, J., Nathwani, J., (2011). Concept Development for Smart Energy Networks
  198. Rudzki, K., Gomulka, P., Hoang, A.T., (2022). Optimization Model to Manage Ship Fuel Consumption and Navigation Time. Polish Marit. Res. 29, 141–153. https://doi.org/10.2478/pomr-2022-0034
  199. Rumapea, H., Zarlis, M., Efendy, S., Sihombing, P., (2024). Improving Convective Cloud Classification with Deep Learning : The CC-Unet Model. Int. J. Adv. Sci. Eng. Inf. Technol. 14, 28–36
  200. Runge, J., Zmeureanu, R., (2019). Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review. Energies 12, 3254. https://doi.org/10.3390/en12173254
  201. S, S., Basar, A., Wang, H., (2020). Artificial Neural Network Based Power Management for Smart Street Lighting Systems. J. Artif. Intell. Capsul. Networks 2, 42–52. https://doi.org/10.36548/jaicn.2020.1.005
  202. Sai, Z., Lu, C., Jiang, S., Shan, L., James, C., Xiong, N.N., (2020). Energy Management Optimization of Open-Pit Mine Solar Photothermal-Photoelectric Membrane Distillation Using a Support Vector Machine and a Non-Dominated Genetic Algorithm. IEEE Access 8, 155766–155782. https://doi.org/10.1109/ACCESS.2020.3017688
  203. Said, Z., Sharma, P., Thi Bich Nhuong, Q., Bora, B.J., Lichtfouse, E., Khalid, H.M., Luque, R., Nguyen, X.P., Hoang, A.T., (2023). Intelligent approaches for sustainable management and valorisation of food waste. Bioresour. Technol. 377, 128952. https://doi.org/10.1016/j.biortech.2023.128952
  204. Said, Z., Sharma, P., Tiwari, A.K., Le, V.V., Huang, Z., Bui, V.G., Hoang, A.T., (2022). Application of novel framework based on ensemble boosted regression trees and Gaussian process regression in modelling thermal performance of small-scale Organic Rankine Cycle (ORC) using hybrid nanofluid. J. Clean. Prod. 360, 132194. https://doi.org/10.1016/j.jclepro.2022.132194
  205. Sameti, M., Jokar, M.A., Astaraei, F.R., (2017). Prediction of solar Stirling power generation in smart grid by GA-ANN model. Int. J. Comput. Appl. Technol. 55, 147. https://doi.org/10.1504/IJCAT.2017.082860
  206. Sarwosri, S., Rochimah, S., Laili Yuhana, U., Balqis Hidayat, S., (2023). Software Quality Measurement for Functional Suitability, Performance Efficiency, and Reliability Characteristics Using Analytical Hierarchy Process. JOIV Int. J. Informatics Vis. 7, 2421–2426. https://doi.org/10.30630/joiv.7.4.02441
  207. Seutche, R.V.N., Sawadogo, M., Ngassam, F.N., (2021). Valuation of CO2 Emissions Reduction from Renewable Energy and Energy Efficiency Projects in Africa: A Case Study of Burkina Faso. Int. J. Renew. Energy Dev. 10, 713–729. https://doi.org/10.14710/ijred.2021.34566
  208. Shan, W., Qiao, Z., Heidari, A.A., Chen, H., Turabieh, H., Teng, Y., (2021). Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis. Knowledge-Based Syst. 214, 106728. https://doi.org/10.1016/j.knosys.2020.106728
  209. Shao, M., Wang, X., Bu, Z., Chen, X., Wang, Y., (2020). Prediction of energy consumption in hotel buildings via support vector machines. Sustain. Cities Soc. 57, 102128. https://doi.org/10.1016/j.scs.2020.102128
  210. Shao, Z., Zheng, Q., Yang, S., Gao, F., Cheng, M., Zhang, Q., Liu, C., (2020). Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM. Energy Econ. 86, 104648. https://doi.org/10.1016/j.eneco.2019.104648
  211. Sharma, P., Sahoo, B.B., Said, Z., Hadiyanto, H., Nguyen, X.P., Nižetić, S., Huang, Z., Hoang, A.T., Li, C., (2023). Application of machine learning and Box-Behnken design in optimizing engine characteristics operated with a dual-fuel mode of algal biodiesel and waste-derived biogas. Int. J. Hydrogen Energy 48, 6738–6760. https://doi.org/10.1016/j.ijhydene.2022.04.152
  212. Sharma, P., Said, Z., Kumar, A., Nižetić, S., Pandey, A., Hoang, A.T., Huang, Z., Afzal, A., Li, C., Le, A.T., Nguyen, X.P., Tran, V.D., (2022a). Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System. Energy & Fuels 36, 6626–6658. https://doi.org/10.1021/acs.energyfuels.2c01006
  213. Sharma, P., Said, Z., Memon, S., Elavarasan, R.M., Khalid, M., Nguyen, X.P., Arıcı, M., Hoang, A.T., Nguyen, L.H., (2022b). Comparative evaluation of AI‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe3O4‐coated MWCNT hybrid nanofluids for potential application in energy systems. Int. J. Energy Res. 46, 19242–19257. https://doi.org/10.1002/er.8010
  214. Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B., Liu, D., (2016). Evolving support vector machines using fruit fly optimization for medical data classification. Knowledge-Based Syst. 96, 61–75. https://doi.org/10.1016/j.knosys.2016.01.002
  215. Shi, W., Li, N., Chu, C.-C., Gadh, R., (2017). Real-Time Energy Management in Microgrids. IEEE Trans. Smart Grid 8, 228–238. https://doi.org/10.1109/TSG.2015.2462294
  216. Soares, J., Sousa, T., Morais, H., Vale, Z., Faria, P., (2011). An optimal scheduling problem in distribution networks considering V2G, in: 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG). IEEE, pp. 1–8. https://doi.org/10.1109/CIASG.2011.5953342
  217. Sousa, T., Morais, H., Vale, Z., Faria, P., Soares, J., (2012). Intelligent Energy Resource Management Considering Vehicle-to-Grid: A Simulated Annealing Approach. IEEE Trans. Smart Grid 3, 535–542. https://doi.org/10.1109/TSG.2011.2165303
  218. Statista, (2019). Global renewable energy industry
  219. Statista, (2013). Energy supply, in: OECD Factbook 2013: Economic, Environmental and Social Statistics. https://doi.org/10.1787/factbook-2013-41-en
  220. Sun, M., Zhang, T., Wang, Y., Strbac, G., Kang, C., (2020). Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting. IEEE Trans. Power Syst. 35, 188–201. https://doi.org/10.1109/TPWRS.2019.2924294
  221. Sutton, R.S., Barto, A.G., (2018). Reinforcement learning: An introduction. MIT press
  222. Suvon, I.H., Loh, Y.P., Hashim, N., Mohd-Isa, W.N., Ting, C.Y., Ghauth, K.I., Bhattacharijee, A., Matsah, W.R., (2023). Business Category Classification via Indistinctive Satellite Image Analysis Using Deep Learning. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 2219–2230. https://doi.org/10.18517/ijaseit.13.6.19059
  223. Tabanjat, A., Becherif, M., Hissel, D., Ramadan, H.S., (2018). Energy management hypothesis for hybrid power system of H 2 /WT/PV/GMT via AI techniques. Int. J. Hydrogen Energy 43, 3527–3541. https://doi.org/10.1016/j.ijhydene.2017.06.085
  224. Tan, Y., Liu, W., Qiu, Q., (2009). Adaptive power management using reinforcement learning, in: Proceedings of the 2009 International Conference on Computer-Aided Design. pp. 461–467
  225. Tang, L., Zhao, Y., Liu, J., (2014). An Improved Differential Evolution Algorithm for Practical Dynamic Scheduling in Steelmaking-Continuous Casting Production. IEEE Trans. Evol. Comput. 18, 209–225. https://doi.org/10.1109/TEVC.2013.2250977
  226. Tarasiuk, T., Cao, W., Geng, P., Xu, X., (2023). Energy management strategy considering energy storage system degradation for hydrogen fuel cell ship. Polish Marit. Res. 30, 95–104. https://doi.org/10.2478/pomr-2023-0025
  227. Tascikaraoglu, A., Boynuegri, A.R., Uzunoglu, M., (2014). A demand side management strategy based on forecasting of residential renewable sources: A smart home system in Turkey. Energy Build. 80, 309–320. https://doi.org/10.1016/j.enbuild.2014.05.042
  228. Tian, H., Wang, X., Lu, Z., Huang, Y., Tian, G., (2018). Adaptive Fuzzy Logic Energy Management Strategy Based on Reasonable SOC Reference Curve for Online Control of Plug-in Hybrid Electric City Bus. IEEE Trans. Intell. Transp. Syst. 19, 1607–1617. https://doi.org/10.1109/TITS.2017.2729621
  229. Tian, X., Cai, Y., Sun, X., Zhu, Z., Xu, Y., (2019). An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses. Energy 189, 116151. https://doi.org/10.1016/j.energy.2019.116151
  230. Triandi, B., Efendi, S., Mawengkang, H., Sawaluddin, (2023). Regression-based Analytical Approach for Speech Emotion Prediction based on Multivariate Additive Regression Spline (MARS). Int. J. Adv. Sci. Eng. Inf. Technol. 13, 2213–2218. https://doi.org/10.18517/ijaseit.13.6.18603
  231. Tsui, K.M., Chan, S.C., (2012). Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing. IEEE Trans. Smart Grid 3, 1812–1821. https://doi.org/10.1109/TSG.2012.2218835
  232. Tu, J., Chen, H., Liu, J., Heidari, A.A., Zhang, X., Wang, M., Ruby, R., Pham, Q.-V., (2021). Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance. Knowledge-Based Syst. 212, 106642. https://doi.org/10.1016/j.knosys.2020.106642
  233. Tuan Hoang, A., Nižetić, S., Chyuan Ong, H., Tarelko, W., Viet Pham, V., Hieu Le, T., Quang Chau, M., Phuong Nguyen, X., (2021). A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels. Sustain. Energy Technol. Assessments 47, 101416. https://doi.org/10.1016/j.seta.2021.101416
  234. Ugwu, J., Odo, K.C., Oluka, L.O., Salami, K.O., (2022). A Systematic Review on the Renewable Energy Development, Policies and Challenges in Nigeria with an International Perspective and Public Opinions. Int. J. Renew. Energy Dev. 11, 287–308. https://doi.org/10.14710/ijred.2022.40359
  235. Ullah, Z., Baseer, M., (2022). Operational Planning and Design of Market-Based Virtual Power Plant with High Penetration of Renewable Energy Sources. Int. J. Renew. Energy Dev. 11, 620–629. https://doi.org/10.14710/ijred.2022.44586
  236. Vaisakh, T., Jayabarathi, R., (2022). Analysis on intelligent machine learning enabled with meta-heuristic algorithms for solar irradiance prediction. Evol. Intell. 15, 235–254. https://doi.org/10.1007/s12065-020-00505-6
  237. Vasant, P., Marmolejo, J.A., Litvinchev, I., Aguilar, R.R., (2020). Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle. Wirel. Networks 26, 4753–4766. https://doi.org/10.1007/s11276-019-01993-w
  238. Venayagamoorthy, G.K., Sharma, R.K., Gautam, P.K., Ahmadi, A., (2016). Dynamic Energy Management System for a Smart Microgrid. IEEE Trans. Neural Networks Learn. Syst. 27, 1643–1656. https://doi.org/10.1109/TNNLS.2016.2514358
  239. Veza, I., Afzal, A., Mujtaba, M.A., Tuan Hoang, A., Balasubramanian, D., Sekar, M., Fattah, I.M.R., Soudagar, M.E.M., EL-Seesy, A.I., Djamari, D.W., Hananto, A.L., Putra, N.R., Tamaldin, N., (2022a). Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine. Alexandria Eng. J. 61, 8363–8391. https://doi.org/10.1016/j.aej.2022.01.072
  240. Veza, I., Karaoglan, A.D., Ileri, E., Kaulani, S.A., Tamaldin, N., Latiff, Z.A., Muhamad Said, M.F., Hoang, A.T., Yatish, K.V., Idris, M., (2022b). Grasshopper optimization algorithm for diesel engine fuelled with ethanol-biodiesel-diesel blends. Case Stud. Therm. Eng. 31, 101817. https://doi.org/10.1016/j.csite.2022.101817
  241. Vo, D.T., Nguyen, X.P., Nguyen, T.D., Hidayat, R., Huynh, T.T., Nguyen, D.T., (2021). A review on the internet of thing (IoT) technologies in controlling ocean environment. Energy Sources, Part A Recover. Util. Environ. Eff. 1–19. https://doi.org/10.1080/15567036.2021.1960932
  242. Wang, D., Ge, S., Jia, H., Wang, C., Zhou, Y., Lu, N., Kong, X., (2014). A Demand Response and Battery Storage Coordination Algorithm for Providing Microgrid Tie-Line Smoothing Services. IEEE Trans. Sustain. Energy 5, 476–486. https://doi.org/10.1109/TSTE.2013.2293772
  243. Wang, H., Huang, J., Lin, X., Mohsenian-Rad, H., (2016). Proactive Demand Response for Data Centers: A Win-Win Solution. IEEE Trans. Smart Grid 7, 1584–1596. https://doi.org/10.1109/TSG.2015.2501808
  244. Wang, M., Chen, H., (2020). Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl. Soft Comput. 88, 105946. https://doi.org/10.1016/j.asoc.2019.105946
  245. Wang, M., Chen, H., Yang, B., Zhao, X., Hu, L., Cai, Z., Huang, H., Tong, C., (2017). Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267, 69–84. https://doi.org/10.1016/j.neucom.2017.04.060
  246. Wang, Y., Huang, Z., (2020). Optimization-Based Energy Management Strategy for a 48-V Mild Parallel Hybrid Electric Power System. J. Energy Resour. Technol. 142. https://doi.org/10.1115/1.4045866
  247. Wasista, S., Tjandrasa, H., Djanali, S., (2023). Design an Intelligent Balanced Control of Quadruped Legs Based on Adaptive Neuro-Fuzzy Inference System (ANFIS). Int. J. Adv. Sci. Eng. Inf. Technol. 13, 901–910. https://doi.org/10.18517/ijaseit.13.3.18589
  248. Wattana, B., Aungyut, P., (2022). Impacts of Solar Electricity Generation on the Thai Electricity Industry. Int. J. Renew. Energy Dev. 11, 157–163. https://doi.org/10.14710/ijred.2022.41059
  249. Wedashwara, W., Yadnya, M.S., Sudiarta, I.W., Arimbawa, I.W.A., Mulyana, T., (2023). Solar Powered Vibration Propagation Analysis System using nRF24l01 based WSN and FRBR. JOIV Int. J. Informatics Vis. 7, 15–21. https://doi.org/10.30630/joiv.7.1.1592
  250. Wei, Q., Liu, D., Shi, G., (2015). A novel dual iterative Q-learning method for optimal battery management in smart residential environments. IEEE Trans. Ind. Electron. 62, 2509–2518. https://doi.org/10.1109/TIE.2014.2361485
  251. Wei, T., Wang, Y., Zhu, Q., (2017). Deep Reinforcement Learning for Building HVAC Control, in: Proceedings of the 54th Annual Design Automation Conference 2017. ACM, New York, NY, USA, pp. 1–6. https://doi.org/10.1145/3061639.3062224
  252. Wen, Z., O’Neill, D., Maei, H., (2015). Optimal Demand Response Using Device-Based Reinforcement Learning. IEEE Trans. Smart Grid 6, 2312–2324. https://doi.org/10.1109/TSG.2015.2396993
  253. Whitehead, B., Andrews, D., Shah, A., Maidment, G., (2015). Assessing the environmental impact of data centres part 2: Building environmental assessment methods and life cycle assessment. Build. Environ. 93, 395–405. https://doi.org/10.1016/j.buildenv.2014.08.015
  254. Witkowska, A., Rynkiewicz, T.N., (2018). Dynamically Positioned Ship Steering Making Use of Backstepping Method and Artificial Neural Networks. Polish Marit. Res. 25, 5–12. https://doi.org/10.2478/pomr-2018-0126
  255. Wu, C.-H., Ho, J.-M., Lee, D.T., (2004). Travel-Time Prediction With Support Vector Regression. IEEE Trans. Intell. Transp. Syst. 5, 276–281. https://doi.org/10.1109/TITS.2004.837813
  256. Xiao, F., Fan, C., (2014). Data mining in building automation system for improving building operational performance. Energy Build. 75, 109–118. https://doi.org/10.1016/j.enbuild.2014.02.005
  257. Xie, S., Hu, X., Qi, S., Lang, K., (2018). An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles. Energy 163, 837–848. https://doi.org/10.1016/j.energy.2018.08.139
  258. Xin, L., Chuanzhi, Z., Peng, Z., Haibin, Y., (2012). Genetic based fuzzy Q-learning energy management for smart grid, in: Proceedings of the 31st Chinese Control Conference. IEEE, pp. 6924–6927
  259. Xu, X., Chen, H., (2014). Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Comput. 18, 797–807. https://doi.org/10.1007/s00500-013-1089-4
  260. Xu, Y., Chen, H., Luo, J., Zhang, Q., Jiao, S., Zhang, X., (2019). Enhanced Moth-flame optimizer with mutation strategy for global optimization. Inf. Sci. (Ny). 492, 181–203. https://doi.org/10.1016/j.ins.2019.04.022
  261. Yacim, J.A., Boshoff, D.G.B., (2020). Neural networks support vector machine for mass appraisal of properties. Prop. Manag. 38, 241–272. https://doi.org/10.1108/PM-09-2019-0053
  262. Yadav, A.K., Malik, H., Chandel, S.S., (2015). Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India. Renew. Sustain. Energy Rev. 52, 1093–1106. https://doi.org/10.1016/j.rser.2015.07.156
  263. Yaïci, W., Longo, M., Entchev, E., Foiadelli, F., (2017). Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System. Sustainability 9, 1382. https://doi.org/10.3390/su9081382
  264. Yan, J., Menghwar, M., Asghar, E., Kumar Panjwani, M., Liu, Y., (2019). Real-time energy management for a smart-community microgrid with battery swapping and renewables. Appl. Energy 238, 180–194. https://doi.org/10.1016/j.apenergy.2018.12.078
  265. Yan, T., Liu, J., Niu, Q., Chen, J., Xu, S., Niu, M., Lin, J.Y.S., (2020a). Distributed energy storage node controller and control strategy based on energy storage cloud platform architecture. Glob. Energy Interconnect. 3, 166–174. https://doi.org/10.1016/j.gloei.2020.05.008
  266. Yan, T., Liu, J., Niu, Q., Chen, J., Xu, S., Niu, M., Lin, J.Y.S., (2020b). Distributed energy storage node controller and control strategy based on energy storage cloud platform architecture. Glob. Energy Interconnect. 3, 166–174. https://doi.org/10.1016/j.gloei.2020.05.008
  267. Yavasoglu, H.A., Tetik, Y.E., Ozcan, H.G., (2020a). Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle. Int. J. Energy Res. 44, 12416–12429. https://doi.org/10.1002/er.5429
  268. Yavasoglu, H.A., Tetik, Y.E., Ozcan, H.G., (2020b). Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle. Int. J. Energy Res. 44, 12416–12429. https://doi.org/10.1002/er.5429
  269. Yazdanian, M., Mehrizi-Sani, A., (2014). Distributed Control Techniques in Microgrids. IEEE Trans. Smart Grid 5, 2901–2909. https://doi.org/10.1109/TSG.2014.2337838
  270. Yousefi, S., Moghaddam, M.P., Majd, V.J., (2011). Optimal real time pricing in an agent-based retail market using a comprehensive demand response model. Energy 36, 5716–5727. https://doi.org/10.1016/j.energy.2011.06.045
  271. Yu, C., Chen, M., Cheng, K., Zhao, X., Ma, C., Kuang, F., Chen, H., (2022). SGOA: annealing-behaved grasshopper optimizer for global tasks. Eng. Comput. 38, 3761–3788. https://doi.org/10.1007/s00366-020-01234-1
  272. Yuan, R., Li, Z., Guan, X., Xu, L., (2010). An SVM-based machine learning method for accurate internet traffic classification. Inf. Syst. Front. 12, 149–156. https://doi.org/10.1007/s10796-008-9131-2
  273. Yunidar, Y., Melinda, M., (2023). Position and Temperature Detector for Autism Spectrum Disorder Children based on Sensor and Using IoT System. Int. J. Adv. Sci. Eng. Inf. Technol. 13, 2105–2111. https://doi.org/10.18517/ijaseit.13.6.19416
  274. Zadeh, L.A., (1965). Fuzzy sets. Inf. Control 8, 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
  275. Zafar, A., Shah, S., Khalid, R., Hussain, S.M., Rahim, H., Javaid, N., (2017). A Meta-Heuristic Home Energy Management System, in: 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, pp. 244–250. https://doi.org/10.1109/WAINA.2017.118
  276. Zaki, M., (2024). Effectiveness of Vehicular Communication Using NP-CSMA with Bernoulli-Based Gaussian Interpolation Function. Int. J. Adv. Sci. Eng. Inf. Technol. 14, 89–98
  277. Zendehboudi, A., Baseer, M.A., Saidur, R., (2018). Application of support vector machine models for forecasting solar and wind energy resources: A review. J. Clean. Prod. 199, 272–285. https://doi.org/10.1016/j.jclepro.2018.07.164
  278. Zeng, P., Li, H., He, H., Li, S., (2019). Dynamic Energy Management of a Microgrid Using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning. IEEE Trans. Smart Grid 10, 4435–4445. https://doi.org/10.1109/TSG.2018.2859821
  279. Zhang, A., Zhang, P., Feng, Y., (2019). Short-term load forecasting for microgrids based on DA-SVM. COMPEL - Int. J. Comput. Math. Electr. Electron. Eng. 38, 68–80. https://doi.org/10.1108/COMPEL-05-2018-0221
  280. Zhang, G., Sun, H., (2016). Secure Distributed Detection under Energy Constraint in IoT-Oriented Sensor Networks. Sensors 16, 2152. https://doi.org/10.3390/s16122152
  281. Zhang, L., Qiu, Y., Chen, Y., Hoang, A.T., (2023). Multi-objective particle swarm optimization applied to a solar-geothermal system for electricity and hydrogen production; Utilization of zeotropic mixtures for performance improvement. Process Saf. Environ. Prot. 175, 814–833. https://doi.org/10.1016/j.psep.2023.05.082
  282. Zhang, Y., Liu, R., Wang, X., Chen, H., Li, C., (2021). Boosted binary Harris hawks optimizer and feature selection. Eng. Comput. 37, 3741–3770. https://doi.org/10.1007/s00366-020-01028-5
  283. Zhang, Y., Salem, M., Elmasry, Y., Hoang, A.T., Galal, A.M., Pham Nguyen, D.K., Wae-hayee, M., (2022). Triple-objective optimization and electrochemical/technical/environmental study of biomass gasification process for a novel high-temperature fuel cell/electrolyzer/desalination scheme. Renew. Energy 201, 379–399. https://doi.org/10.1016/j.renene.2022.10.059
  284. Zhao, X., Zhang, X., Cai, Z., Tian, X., Wang, X., Huang, Y., Chen, H., Hu, L., (2019). Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Comput. Biol. Chem. 78, 481–490. https://doi.org/10.1016/j.compbiolchem.2018.11.017
  285. Zhao, Y., Moayedi, H., Bahiraei, M., Foong, L.K., (2020). Employing TLBO and SCE for optimal prediction of the compressive strength of concrete. Smart Struct. Syst. 26, 753–763
  286. Zheng Chen, Mi, C.C., Jun Xu, Xianzhi Gong, Chenwen You, (2014). Energy Management for a Power-Split Plug-in Hybrid Electric Vehicle Based on Dynamic Programming and Neural Networks. IEEE Trans. Veh. Technol. 63, 1567–1580. https://doi.org/10.1109/TVT.2013.2287102
  287. Zhu, M., (2021). Implementation of support-vector machine algorithm to develop a model for electronic commerce energy regulatory system. Energy Reports 7, 2703–2710. https://doi.org/10.1016/j.egyr.2021.05.009
  288. Zhu, W., Hou, Y., Wang, E., Wang, Y., (2020). Design of Geographic Information Visualization System for Marine Tourism Based on Data Mining. J. Coast. Res. 103, 1034. https://doi.org/10.2112/SI103-215.1
  289. Zhuang Zhao, Won Cheol Lee, Yoan Shin, Kyung-Bin Song, (2013). An Optimal Power Scheduling Method for Demand Response in Home Energy Management System. IEEE Trans. Smart Grid 4, 1391–1400. https://doi.org/10.1109/TSG.2013.2251018

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