پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 717 |
| تعداد مقالات | 10,307 |
| تعداد مشاهده مقاله | 72,269,792 |
| تعداد دریافت فایل اصل مقاله | 63,999,699 |
Optimization of energy consumption in smart city using reinforcement learning algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 23، دوره 15، شماره 1، فروردین 2024، صفحه 277-290 اصل مقاله (1.19 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.29258.4102 | ||
| نویسندگان | ||
| Mohammad Ordouei1؛ Ali Broumandnia* 1؛ Touraj Banirostam2؛ Alireza Gilani3 | ||
| 1Department of Computer, South Tehran Branch, Islamic Azad University, Tehran Iran | ||
| 2Department of Technical and Engineering, Central Tehran Branch, Islamic Azad University, Tehran Iran | ||
| 3Department of Mathematics, South Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| تاریخ دریافت: 28 فروردین 1401، تاریخ بازنگری: 21 خرداد 1401، تاریخ پذیرش: 27 تیر 1401 | ||
| چکیده | ||
| One of the most important challenges facing the evolution of smart cities over the last decade has been the optimization of energy use. Also, artificial intelligence and its algorithms, such as reinforcement learning, have appeared as a catalyst in the process of designing and optimizing smart services in the urban space, and in this issue, the generation and use of energy are critical factors. Using a technique based on reinforcement learning, the authors of this research successfully decreased and optimised smart city energy use. The suggested reinforcement learning method uses a collection of agents to cooperate together to achieve a shared objective using an optimum energy distribution policy (value action function). Agents' ability to cooperate to optimise energy use and save expenses is only one example of the many advantages that will accrue from their concerted efforts. To determine the worth of each option, the suggested technique looks at energy consumption data and the degree to which the option has been implemented in the past. This architecture ensures the device achieves an optimal balance between its energy footprint and the dependability of its communications. The simulation findings reveal that the yearly energy consumption in the smart city may be reduced by more than 35%-40% via the optimization of energy consumption using the proposed reinforcement learning approach. | ||
| کلیدواژهها | ||
| Keywords: Reinforcement learning؛ Energy Optimization؛ Smart city | ||
| مراجع | ||
|
[1] M. Alazab, K. Lakshmanna, Q.V. Pham and P.K. Reddy Maddikunta, Multi-objective cluster head selection using fitness averaged rider optimization algorithm for IoT networks in smart cities, Sustain. Energy Technol. Assess. 43 (2021), 100973. [2] V. Albino and U. Berardi, Dangelico RM (2015) Smart cities: definitions, dimensions, performance, and initiatives, J. Urban Technol. 22 (2015), 1–19. [3] G. Amelie, M. Serrano and Gh.A. Atemezing, Semantic web methodologies, best practices and ontology engineering applied to Internet of Things, IEEE 2nd World Forum on Internet of Things (WF-IoT), 2015, pp. 412–417. [4] F. Aymen and C. Mahmoudi, A novel energy optimization approach for electrical vehicles in a smart city, Energies. 12(5) (2019). [5] A. Azizivahed, A. Arefi, S. Ghavidel, M. Shafie-khah, L. Li, J. Zhang, and J.P.S. Catalão, Energy management strategy in dynamic distribution network reconfiguration considering renewable energy resources and storage, IEEE Trans. Sustain. Energy 11 (2020), no. 2, 662–673. [6] T. Banirostam and M.N. Fesharaki, A new approach for biological complex adaptive system modeling and simulation, Life Sci. J. 9 (2012), no. 3, 2257–2263. [7] T. Banirostam and M.N. Fesharaki, Effective parameters in convergence of autonomous distributed systems using with immune system approach, 10th Int. Symp. Autonomous Decentr. Syst. (ISADS-IEEE), 2011, pp. 204–208. [8] C.F. Calvillo, A. Sanchez-Miralles and J. Villar, Energy management and planning in smart cities, Renew. Sustain. Energy Rev. 55 (2016), 273–280. [9] H. Carlos, E. Biele, Ch. Martini, M. Salvio and C. Toro, Impact of energy monitoring and management systems on the implementation and planning of energy performance improved actions: An empirical analysis based on energy audits in Italy, Energies 14 (2021), no. 16, 4723. [10] P. Chithaluru, F. Al-Turjman, M. Kumar and T. Stephan, I-AREOR: An energy-balanced clustering protocol for implementing green IoT in smart cities, Sustain. Cities Soc. 61 (2020), 102254. [11] G. Gianfranco, M. Lupia, G. Cario, F. Tedesco, F. Cicchello Gaccio, F. Lo Scudo and A. Casavola, Advanced adaptive street lighting systems for smart cities, Smart Cities 3 (2020), no. 4, 1495–1512. [12] M. Glavic, Agents and multi-agent systems: A short introduction for power engineers, University of Liege -Electrical engineering and computer science department, 2006. [13] Y. Guo, Q. Wu, H. Gao and F. Shen, Distributed voltage regulation of smart distribution networks: Consensus based information synchronization and distributed model predictive control scheme, Int. J. Electric. Power Energy Syst. 111 (2019), 58–65. [14] S.M.R. Hashemi, H. Hassanpour, E. Kozegar and T. Tan, Cystoscopy image classification using deep convolutional neural networks, Int. J. Nonlinear Anal. Appl. 10 (2019), no. 1, 193–215. [15] Y. Hayashi, Y. Fujimoto, H. Ishii, Y. Takenobu, H. Kikusato and Sh. Yoshiza, Versatile modeling platform for cooperative energy management systems in smart cities, Proc. IEEE 106 (2018), no. 4, 594–612. [16] A. Kari and S. Sierla, An overview of machine learning applications for smart buildings, Sustain. Cities Soc. 76 (2022), 103445. [17] M.I. Khalil, N.Z. Jhanjhi, M. Humayun, S. Sivanesan, M. Masud and M.S. Hossain, Hybrid smart grid with sustainable energy efficient resources for smart cities, Sustain. Energy Technol. Assess. 46 (2021), 101211. [18] Y. Liu, C. Yang, L. Jiang, S. Xie and Y. Zhang, Intelligent edge computing for IoT-based energy management in smart cities, IEEE Networks 33 (2019), no. 2, 111–117. [19] Z. Magubane, P. Tarwireyi and M.O. Adigun, Evaluating the energy efficiency of IoT routing protocols, Proc. 2019 Int. Multidiscip. Inf. Technol. Engin. Conf.(IMITEC), Vanderbijlpark, South Africa, 21–22 November 2019; IEEE: Piscataway, NJ, USA, 2019, pp. 1–7. [20] A. Mathiesen, B. Vad, H. Lund, D. Connolly, H. Wenzel, P. Alberg Qstergaard, B. Moller and S. Nielsen, I. Ridjan, P. Karnoe, K. Sperling and F.K. Hvelplund, Smart Energy Systems for coherent 100% renewable energy and transport solutions, Appl. Energy 145 (2015), 139–154. [21] S. McClellan, J.A. Jimenez and G. Koutitas, Smart Cities: Applications, Technologies, Standards, and Driving Factors, Springer International Publishing, 2017. [22] E. Mlecnik, J. Parker, Z. Ma, C. Corchero, A. Knotzer and R. Pernetti, Policy challenges for the development of energy flexibility services, Energy Policy 137 (2020), 111147. [23] P. Neamatollahi, M. Naghibzadeh and S. Abrishami, Distributed clustering-task scheduling for wireless sensor networks using dynamic hyper round policy, IEEE Trans. Mob. Comput. 17 (2017), 334–347. [24] M. Ordouei and T. BaniRostam, Integrating data mining and knowledge management to improve customer relationship management in banking industry (Case study of Caspian credit institution), Int. J. Comput. Sci. Network 7 (2018), no. 3, 208–214. [25] M. Ordouei and T. Banirostam, Diagnosis of liver fibrosis using RBF neural network and artificial bee colony algorithm, Int. J. Adv. Res. Comput. Commun. Engin. 11 (2022), no. 12, 45–50. [26] E. Petritoli, F. Leccese, S. Pizzuti and F. Pieroni, Smart lighting as basic building block of smart city: An energy performance comparative case study, Measurement 136 (2019), 466–477. [27] P. Pirozmand, A. Javadpour, H. Nazarian, P. Pinto, S.S. Mirkamali and F. Jafari, GSAGA: A hybrid algorithm for task scheduling in cloud infrastructure, J. Supercomput. 78 (2022), no. 15, 17423–17449. [28] K. Pourjavan, Explanation of smart city and smart transportation solutions, Karafan 16 (2019), no. 1, 15–35. [29] N.P. Rana, S. Luthra, S.K. Mangla, R. Islam, S. Roderick and Y.K. Dwivedi, Barriers to the development of smart cities in Indian context, Inf. Syst. Front. 21 (2019), 503–525. [30] S. Rasaneh and T. Banirostam, A new structure and routing algorithm for optimizing energy consumption in wireless sensor network for disaster management, 4th Int. Conf. Intell. Syst. Model. Simul., 2013, pp. 481–485. [31] M. Stonebraker, U. C， etintemel and S. Zdonik, The 8 requirements of real-time stream processing, ACM Sigmod Record. 34 (2005), no. 4, 42–47. [32] S. Tanwar, A. Popat, P. Bhattacharya, R. Gupta and N. Kumar, A taxonomy of energy optimization techniques for smart cities: Architecture and future directions, Expert Syst. 39 (2022). [33] K. Thangaramya, K. Kulothungan, R. Logambigai, M. Selvi, S. Ganapathy and A. Kannan, Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT, Comput. Netw. 151 (2019), 211–223. [34] T. Yigitcanlar, M. Kamruzzaman, L. Buys, G. Ioppolo, J. Sabatini-Marques, E. Moreira da Costa and J. Joseph Yun, Understanding ‘smart cities’: Intertwining development drivers with desired outcomes in a multidimensional framework, Cities 81 (2018), 145–160. [35] M. Zekic-Susac, S. Mitrovic, and A. Has, Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities, Int. J. Inf. Manag. 58 (2021), 102074. [36] X. Zhang, G. Manogaran and B. Muthu, IoT enabled integrated system for green energy into smart cities, Sustain. Energy Technol. Assess. 46 (2021), 101208. [37] S. Zhexuan, A. Cardenas and R. Masuoka, Semantic middleware for the Internet of things, Internet Things (IoT), IEEE, 2010, pp. 1–8. [38] X. Zhu, J. Wang, N. Lu, N. Samaan, R. Huang and X. Ke, A hierarchical VLSM-based demand response strategy for coordinative voltage control between transmission and distribution systems, IEEE Transactions on Smart Grid. 10 (2019), no. 5, 4838–4847. | ||
|
آمار تعداد مشاهده مقاله: 27,757 تعداد دریافت فایل اصل مقاله: 17,725 |
||