skip to main content

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

View all affiliations
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.

Citation Format:

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.  

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

Article Metrics:

  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  18. Alsafasfeh, Q., (2020). An Efficient Algorithm for Power Prediction in PV Generation System. Int. J. Renew. Energy Dev. 9, 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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  28. Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M., (2020). Monarch Butterfly Optimization Based Convolutional Neural Network Design. Mathematics 8, 936.
  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.
  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.
  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.
  32. Barrett, E., Linder, S., (2015). Autonomous HVAC Control, A Reinforcement Learning Approach. pp. 3–19.
  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.
  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.
  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.
  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.
  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.
  39. Bogaraj, T., Kanakaraj, J., (2016). Intelligent energy management control for independent microgrid. Sādhanā 41, 755–769.
  40. Boudoudouh, S., Maâroufi, M., (2018). Multi agent system solution to microgrid implementation. Sustain. Cities Soc. 39, 252–261.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  64. Corizzo, R., Ceci, M., Fanaee-T, H., Gama, J., (2021). Multi-aspect renewable energy forecasting. Inf. Sci. (Ny). 546, 701–722.
  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.
  66. de Ville, B., (2013). Decision trees. Wiley Interdiscip. Rev. Comput. Stat. 5, 448–455.
  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.
  69. Domachowski, Z., (2021). Minimizing Greenhouse Gas Emissions From Ships Using a Pareto Multi-Objective Optimization Approach. Polish Marit. Res. 28, 96–101.
  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.
  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.
  72. Doukas, H., Patlitzianas, K.D., Iatropoulos, K., Psarras, J., (2007). Intelligent building energy management system using rule sets. Build. Environ. 42, 3562–3569.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  88. Gao, J.B., Gunn, S.R., Harris, C.J., (2003). SVM regression through variational methods and its sequential implementation. Neurocomputing 55, 151–167.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  102. Hu, B., (2018). Application of Evaluation Algorithm for Port Logistics Park Based on Pca-Svm Model. Polish Marit. Res. 25, 29–35.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  114. Jiang, P., Li, R., Lu, H., Zhang, X., (2020). Modeling of electricity demand forecast for power system. Neural Comput. Appl. 32, 6857–6875.
  115. Jo, T., (2021). Machine Learning Foundations. Springer International Publishing, Cham.
  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.
  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.
  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.
  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.
  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.
  121. Kim, M., Choi, W., Jeon, Y., Liu, L., (2019). A Hybrid Neural Network Model for Power Demand Forecasting. Energies 12, 931.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  134. Li, D., Jayaweera, S.K., (2015). Machine-Learning Aided Optimal Customer Decisions for an Interactive Smart Grid. IEEE Syst. J. 9, 1529–1540.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  151. Mason, K., Grijalva, S., (2019). A review of reinforcement learning for autonomous building energy management. Comput. Electr. Eng. 78, 300–312.
  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.
  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.
  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.
  157. Moayedi, H., Mosavi, A., (2021). An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework. Energies 14, 1196.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  171. Nižetić, S., Arıcı, M., Hoang, A.T., (2023). Smart and Sustainable Technologies in Energy Transition. J. Clean. Prod. 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.
  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.
  174. Panait, L., Luke, S., (2005). Cooperative Multi-Agent Learning: The State of the Art. Auton. Agent. Multi. Agent. Syst. 11, 387–434.
  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.
  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.
  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.
  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.
  180. Pérez-Lombard, L., Ortiz, J., Pout, C., (2008). A review on buildings energy consumption information. Energy Build. 40, 394–398.
  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.
  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.
  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.
  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.
  187. Radonjić, A., Pjevčević, D., Maraš, V., (2020). Neural Network Ensemble Approach to Pushed Convoys Dispatching Problems. Polish Marit. Res. 27, 70–82.
  188. Ramesh, T., Prakash, R., Shukla, K.K., (2010). Life cycle energy analysis of buildings: An overview. Energy Build. 42, 1592–1600.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  215. Shi, W., Li, N., Chu, C.-C., Gadh, R., (2017). Real-Time Energy Management in Microgrids. IEEE Trans. Smart Grid 8, 228–238.
  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.
  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.
  218. Statista, (2019). Global renewable energy industry
  219. Statista, (2013). Energy supply, in: OECD Factbook 2013: Economic, Environmental and Social Statistics.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  244. Wang, M., Chen, H., (2020). Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl. Soft Comput. 88, 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.
  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.
  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.
  248. Wattana, B., Aungyut, P., (2022). Impacts of Solar Electricity Generation on the Thai Electricity Industry. Int. J. Renew. Energy Dev. 11, 157–163.
  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.
  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.
  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.
  252. Wen, Z., O’Neill, D., Maei, H., (2015). Optimal Demand Response Using Device-Based Reinforcement Learning. IEEE Trans. Smart Grid 6, 2312–2324.
  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.
  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.
  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.
  256. Xiao, F., Fan, C., (2014). Data mining in building automation system for improving building operational performance. Energy Build. 75, 109–118.
  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.
  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.
  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.
  261. Yacim, J.A., Boshoff, D.G.B., (2020). Neural networks support vector machine for mass appraisal of properties. Prop. Manag. 38, 241–272.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  269. Yazdanian, M., Mehrizi-Sani, A., (2014). Distributed Control Techniques in Microgrids. IEEE Trans. Smart Grid 5, 2901–2909.
  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.
  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.
  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.
  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.
  274. Zadeh, L.A., (1965). Fuzzy sets. Inf. Control 8, 338–353.
  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.
  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.
  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.
  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.
  280. Zhang, G., Sun, H., (2016). Secure Distributed Detection under Energy Constraint in IoT-Oriented Sensor Networks. Sensors 16, 2152.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.

Last update:

No citation recorded.

Last update: 2024-04-13 01:53:10

No citation recorded.