Enhancing Energy Efficiency in Electrical Systems with Reinforcement Learning Algorithms
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Abstract
Improving the energy efficiency of electricity systems is important for lowering environmental damage and promoting sustainable growth. In recent years, reinforcement learning (RL) methods have become useful for finding the best ways to use energy in many areas. The point of this study is to look into how RL algorithms can be used to make electricity systems more energy efficient. The study looks into how RL algorithms can be used to make electricity systems more efficient by lowering waste, making the best use of energy, and maximizing energy use. The study suggests a new way to use RL methods to change things like power sharing, load scheduling, and resource allocation on the fly in order to keep system performance high while using as little energy as possible. Some important parts of the study method are creating RL models that work with electricity systems and their limitations, as well as coming up with the right payment functions to help people learn how to behave in ways that use less energy. Extensive models and real-world studies on sample electrical systems are used to test how well the suggested method works. According to the study's results, using RL algorithms can lead to big changes in how efficiently energy is used, with cuts in energy use running from [insert exact number range]. The study also shows how flexible and scalable RL-based solutions are when it comes to different system setups and operating scenarios. Overall, this study adds to the growing amount of research on energy efficiency by showing how RL algorithms can be used to solve difficult problems in electrical systems. Practical plans can be made to improve energy efficiency and promote sustainability in a wide range of businesses and uses based on what this study has taught us.
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References
D. Maheswaran, V. Rangaraj, K. K. J. Kailas and W. A. Kumar, "Energy efficiency in electrical systems," 2012 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Bengaluru, India, 2012, pp. 1-6, doi: 10.1109/PEDES.2012.6484460.
B. Boychev, M. Malkovska and S. K. Filipova-Petrakieva, "Enhancing energy efficiency through the implementation of photovoltaics in municipal and household buildings," 2019 11th Electrical Engineering Faculty Conference (BulEF), Varna, Bulgaria, 2019, pp. 1-2, doi: 10.1109/BulEF48056.2019.9030753.
N. Tephiruk, P. Jamjang, A. Taweesap and K. Hongesombut, "Hybrid Energy Storage System to Enhance Efficiency of Renewable Energy Usage," 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Prachuap Khiri Khan, Thailand, 2022, pp. 1-4, doi: 10.1109/ECTI-CON54298.2022.9795622.
H. Singh, M. Seera and M. A. Mohamad Idin, "Electrical energy audit in a Malaysian university - a case study," 2012 IEEE International Conference on Power and Energy (PECon), Kota Kinabalu, Malaysia, 2012, pp. 616-619, doi: 10.1109/PECon.2012.6450288.
Z. D. Deng et al., "Efficient Hydraulic-to-electric Energy Conversion for PCM-based Ocean Thermal Gradient Energy System to Power Uncrewed Underwater Vehicles," OCEANS 2023 - Limerick, Limerick, Ireland, 2023, pp. 1-4, doi: 10.1109/OCEANSLimerick52467.2023.10244366.
B. Asare-Bediako, P. F. Ribeiro and W. L. Kling, "Integrated energy optimization with smart home energy management systems," 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Berlin, Germany, 2012, pp. 1-8, doi: 10.1109/ISGTEurope.2012.6465696.
D. Wang, X. Wu, L. Zhao, Q. Shi, W. Kong and H. Dai, "Research on DC Microgrids in the Context of the Rural Energy Internet," 2023 6th Asia Conference on Energy and Electrical Engineering (ACEEE), Chengdu, China, 2023, pp. 87-91, doi: 10.1109/ACEEE58657.2023.10239631.
W. Alshamalat, M. Alsafasfeh and A. Alhasanat, "A Machine Learning Dataset for Enhancing Energy Efficiency in WSN," 2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan, 2023, pp. 93-98, doi: 10.1109/JEEIT58638.2023.10185813.
S. Jagadeesan, C. N. Ravi, M. Sujatha, S. S. Southry, J. Sundararajan and C. V. K. Reddy, "Machine Learning and IoT based Performance Improvement of Energy Efficiency in Smart Buildings," 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2023, pp. 375-380, doi: 10.1109/ICSCDS56580.2023.10104874.
Ajani, S. N. ., Khobragade, P. ., Dhone, M. ., Ganguly, B. ., Shelke, N. ., & Parati, N. . (2023). Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 546–559
D. Daorui, H. Chang and B. V. Durga Kumar, "Optimized Energy Distribution in Smart Grid System Using Hybrid Machine Learning Techniques," 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS), Penang, Malaysia, 2023, pp. 377-381, doi: 10.1109/ICSECS58457.2023.10256394.
B. M. Ali, "Load Predictions in Electrical Energy Network: Current Knowledge and Future Directions Using Machine Learning," 2023 6th International Conference on Engineering Technology and its Applications (IICETA), Al-Najaf, Iraq, 2023, pp. 589-595, doi: 10.1109/IICETA57613.2023.10351352.
J. P. R and A. S. Pillai, "Enhancing Energy Efficiency of Intensive Computing Applications using Approximate Computing," 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2022, pp. 1231-1236, doi: 10.1109/ICESC54411.2022.9885429.
A. Bagwari et al., "An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning," in IEEE Access, vol. 11, pp. 96343-96362, 2023, doi: 10.1109/ACCESS.2023.3311854.
S. K N, P. T. Devadarshini, R. K N, Deshveer, N. V. S. Suryanarayana and V. R. Kanakala, "IoT-based Smart Home Automation Systems for Energy Conservation," 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 2023, pp. 12-16, doi: 10.1109/I-SMAC58438.2023.10290633.
P. Kosmides, L. Lambrinos, V. Asthenopoulos, K. Demestichas and E. Adamopoulou, "A clustering based approach for energy efficient routing," 2016 IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, 2016, pp. 232-237, doi: 10.1109/ISCC.2016.7543745.
M. B. Hassan, R. A. Saeed, O. Khalifa, E. S. Ali, R. A. Mokhtar and A. A. Hashim, "Green Machine Learning for Green Cloud Energy Efficiency," 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), Sabratha, Libya, 2022, pp. 288-294, doi: 10.1109/MI-STA54861.2022.9837531.
Z. Liu, B. Zhang, Q. Li, F. Zhao, D. Liu and K. Hou, "A Data-Driven Reliability Assessment Method for Composite Power Systems," 2023 3rd International Conference on Energy, Power and Electrical Engineering (EPEE), Wuhan, China, 2023, pp. 1339-1343, doi: 10.1109/EPEE59859.2023.10351888.
J. Zhu, X. Lu, M. Heimann and D. Koutra, "Node proximity is all you need: Unified structural and positional node and graph embedding", Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pp. 163-171, 2021.
A. Chowdhury, G. Verma, C. Rao, A. Swami and S. Segarra, "Unfolding wmmse using graph neural networks for efficient power allocation", IEEE Transactions on Wireless Communications, vol. 20, no. 9, pp. 6004-6017, 2021.
M. Osama, S. El Ramly and B. Abdelhamid, "Binary pso with classification trees algorithm for enhancing power efficiency in 5g networks", Sensors, vol. 22, no. 21, pp. 8570, 2022.
A. Alwarafy, M. Abdallah, B. S. Ciftler, A. Al-Fuqaha and M. Hamdi, "The frontiers of deep reinforcement learning for resource management in future wireless hetnets: Techniques challenges and research directions", IEEE Open Journal of the Communications Society, 2022.