Baziar, Aliasghar and Askari, Mohammadreza and Taherianfard, Elahe and Heydari, Mohammad Hossein and Niknam, Taher (2024): Towards a Cyber-Physical System for Sustainable and Smart Economic Building: A Use Case for Optimizing Water and Energy Consumption.
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Abstract
Optimizing energy and water consumption in smart buildings is a critical challenge for enhancing sustainability and reducing operational costs. This paper presents a Cyber-Physical System (CPS) framework that integrates Deep Reinforcement Learning (DRL) and Genetic Algorithms (GA) for real-time decision-making and resource optimization. The system leverages IoT sensors and actuators to monitor and control building systems such as HVAC, lighting, and water management, continuously adjusting parameters to minimize resource consumption while maximizing efficiency. Key findings from the implementation of the DRL + GA framework include up to 20% reductions in energy and water consumption compared to traditional methods. The proposed approach demonstrates significant cost savings and improved system performance, showcasing its effectiveness in real-time optimization. Additionally, the system adapts dynamically to fluctuating conditions such as weather, occupancy, and energy demand. This work contributes to the development of sustainable building management strategies and lays the foundation for smart city applications. The integration of DRL and GA provides a promising solution for optimizing resource allocation and advancing energy efficiency in urban infrastructures.
Item Type: | MPRA Paper |
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Original Title: | Towards a Cyber-Physical System for Sustainable and Smart Economic Building: A Use Case for Optimizing Water and Energy Consumption |
Language: | English |
Keywords: | yber-Physical System (CPS), Smart Buildings, Energy Optimization, Water Consumption, Deep Reinforcement Learning (DRL), Genetic Algorithms (GA), Real-Time Decision-Making, Resource Efficiency, Sustainability, IoT Sensors and Actuators |
Subjects: | Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q0 - General > Q00 - General Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q40 - General Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q56 - Environment and Development ; Environment and Trade ; Sustainability ; Environmental Accounts and Accounting ; Environmental Equity ; Population Growth |
Item ID: | 123270 |
Depositing User: | Prof. Taher Niknam |
Date Deposited: | 18 Jan 2025 09:12 |
Last Modified: | 18 Jan 2025 09:12 |
References: | [1] Yin, S., Wu, J., Zhao, J., Nogueira, M., & Lloret, J. (2024). Green Buildings: Requirements, Features, Life Cycle, and Relevant Intelligent Technologies. Energy and Cyber-Physical Systems. [2] Dabbaghjamanesh, Morteza, et al. ”A novel distributed cloud-fog based framework for energy management of networked microgrids.” IEEE Transactions on Power Systems 35, no. 4 (2020): 2847-2862. [3] Wang, Boyu, et al. ”Cybersecurity enhancement of power trading within the networked microgrids based on blockchain and directed acyclic graph approach.” IEEE Transactions on Industry Applications 55, no. 6 (2019): 7300-7309. [4] Sen, S., Kumar, M., Vedik, B., & Shiva, C. K. (2024). Adaptive- DMPC Based Energy Management and Pre-installation Techno-economic Analysis of a Grid-Tied Cyber-Physical Community Microgrid. Chaos, Solitons & Fractals. [5] Kavousi-Fard, Abdollah, et al. ”Digital Twin for mitigating solar energy resources challenges: A Perspective Review.” Solar Energy 274 (2024): 112561. [6] Moeini, Amirhossein, et al. ”Artificial neural networks for asymmetric selective harmonic current mitigation-PWM in active power filters to meet power quality standards.” IEEE Transactions on Industry Applications (2020). [7] Dabbaghjamanesh, Morteza, et al. ”Networked microgrid security and privacy enhancement by the blockchain-enabled Internet of Things approach.” In 2019 IEEE Green Technologies Conference (GreenTech), pp. 1-5. IEEE, 2019. [8] Sanwar, A. S. M. (2024). Explainable Artificial Intelligence into Cyber-Physical System Architecture of Smart Cities: Technologies, Challenges, and Opportunities. Journal of Electrical Systems. [9] Esapour, Khodakhast, et al. ”A novel energy management framework incorporating multi-carrier energy hub for smart city.” IET Generation, Transmission & Distribution 17, no. 3 (2023): 655-666. [10] Kavousi-Fard, Abdollah, et al. ”Artificial intelligence and blockchain-enabled decentralized electric vehicle charging management.” Journal of Electrical Engineering & Technology (2023). [11] Qian, M., Qian, C., Xu, G., Tian, P., & Yu, W. (2024). Smart Irrigation Systems from Cyber–Physical Perspective: State of Art and Future Directions. Future Internet. [12] Razmjouei, Pouyan, et al. ”A blockchain-based mutual authentication method to secure the electric vehicles’ TPMS.” IEEE Transactions on Industrial Informatics 20.1 (2023): 158-168. [13] Kavousi-Fard, Abdollah, et al. ”An evolutionary deep learning-based anomaly detection model for securing vehicles.” IEEE Transactions on Intelligent Transportation Systems 22, no. 7 (2020): 4478-4486. [14] Parekh, R. (2024). Automating the Design Process for Smart Building Technologies. World Journal of Advanced Research and Reviews. [15] Dabbaghjamanesh, Morteza, et al. ”Stochastic modeling and integration of plug-in hybrid electric vehicles in reconfigurable microgrids with deep learning-based forecasting.” IEEE Transactions on Intelligent Transportation Systems 22, no. 7 (2020): 4394-4403. [16] Tajmasebi, Dorna, et al. ”A security-preserving framework for sustainable distributed energy transition: Case of smart city.” Renewable Energy Focus 51 (2024): 100631. [17] Mendia, I., Gil-Lopez, S., Grau, I., & Del Ser, J. (2024). A Novel Approach for the Detection of Anomalous Energy Consumption Patterns in Industrial Cyber-Physical Systems. Expert Systems. [18] Razmjouei, Pouyan, et al. ”DAG-based smart contract for dynamic 6G wireless EVs charging system.” IEEE Transactions on Green Communications and Networking 6, no. 3 (2022): 1459-1467. [19] Fard, Abdollah Kavousi, et al. ”Superconducting fault current limiter allocation in reconfigurable smart grids.” In 2019 3rd International Conference on Smart Grid and Smart Cities (ICSGSC), pp. 76-80. IEEE, 2019. [20] Li, G., Yang, Z., Fu, Y., O’Neill, Z., & Ren, L. (2024). A Hardware- in-the-Loop (HIL) Testbed for Cyber-Physical Energy Systems in Smart Commercial Buildings. Energy and Technology for Sustainability. [21] Ashkaboosi, Maryam, et al. ”An optimization technique based on profit of investment and market clearing in wind power systems.” American Journal of Electrical and Electronic Engineering 4, no. 3 (2016): 85-91. [22] Kavousi-Fard, Abdollah, et al. ”IoT-based data-driven fault allocation in microgrids using advanced μPMUs.” Ad Hoc Networks 119 (2021): 102520. [23] Li, G., Ren, L., Pradhan, O., O’Neill, Z., Wen, J., & Yang, Z. (2024). Emulation and Detection of Physical Faults and Cyber-Attacks on Building Energy Systems through Real-Time Hardware-in-the-Loop Experiments. Energy and Buildings. [24] Tajalli, Seyede Zahra, et al. ”DoS-resilient distributed optimal scheduling in a fog supporting IIoT-based smart microgrid.” IEEE Transactions on Industry Applications 56, no. 3 (2020): 2968-2977. [25] Dabbaghjamanesh, Morteza, et al. ”A new efficient stochastic energy management technique for interconnected AC microgrids.” In 2018 IEEE Power Energy Society General Meeting (PESGM), pp. 1-5. IEEE, 2018. [26] Kumar, S., & Bhowmik, B. (2024). Emergence, Evolution, and Applications of Cyber-Physical Systems in Smart Society. IEEE Communications and Sustainable Technologies. [27] Wang, Boyu, et al. ”AI-enhanced multi-stage learning-to-learning approach for secure smart cities load management in IoT networks.” AdHoc Networks 164 (2024): 103628. [28] Dabbaghjamanesh, Morteza, et al. ”Deep learning-based real-time switching of hybrid AC/DC transmission networks.” IEEE Transactions on Smart Grid 12, no. 3 (2020): 2331-2342. [29] Kanso, H., Noureddine, A., & Exposito, E. (2024). A Review of Energy Aware Cyber-Physical Systems. Cyber-Physical Systems. [30] Mohammadi, Hossein, et al. ”AI-based optimal scheduling of renewable AC microgrids with bidirectional LSTM-based wind power forecasting.” arXiv preprint arXiv:2208.04156 (2022). [31] Khazaei, Peyman, et al. ”A high efficiency DC/DC boost converter for photovoltaic applications.” International Journal of Soft Computing and Engineering (IJSCE) 6, no. 2 (2016): 2231-2307. [32] Hu, G., & You, F. (2024). AI-enabled Cyber-Physical-Biological Systems for Smart Energy Management and Sustainable Food Production in a Plant Factory. Applied Energy. [33] Dabbaghjamanesh, Morteza, et al. ”A novel two-stage multi-layer constrained spectral clustering strategy for intentional islanding of power grids.” IEEE Transactions on Power Delivery 35, no. 2 (2019): 560-570. [34] Fard, Abdollah Kavousi, et al. ”Optimal control strategy for hybrid microgrid systems under different energy resource configurations.” Energy Reports 5 (2019): 1024-1031. [35] Hassan, Q., Sarhan, N., Awwad, E. M., & Al-Musawi, T. J. (2024). Smart Building Energy Systems for Sustainable Living: A Realistic Approach to Enhance Renewable Energy Consumption and Reduce Emissions in Residential Buildings. Energy and Buildings. [36] Kavousi-Fard, Abdollah, et al. ”Superconducting fault current limiter allocation in reconfigurable smart grids.” In 2019 3rd International Conference on Smart Grid and Smart Cities (ICSGSC), pp. 76-80. IEEE, 2019. [37] Dabbaghjamanesh, Morteza, et al. ”High performance control of grid connected cascaded H-Bridge active rectifier based on type II-fuzzy logic controller with low frequency modulation technique.” International Journal of Electrical and Computer Engineering 6, no. 2 (2016): 484. [38] Desmond, L., & Salama, M. (2024). Integration of Cyber Physical Systems and Data Science into the Built Environment Lifecycle. International Congress on Information and Communication Technology. [39] Dabbaghjamanesh, Morteza, et al. ”A novel distributed cloud-fog based framework for energy management of networked microgrids.” IEEE Transactions on Power Systems 35, no. 4 (2020): 2847-2862. [40] Dehbozorgi, Mohammad Reza, et al. ”Decision tree-based classifiers for root-cause detection of equipment-related distribution power system outages.” IET Generation, Transmission & Distribution 14, no. 24 (2020): 5809-5815. [41] Moeini, Amirhossein, et al. ”Optimization of wind power generation using artificial neural networks.” Energy Reports 8 (2022): 142-153. [42] Bhadani, U. (2024). Smart Grids: A Cyber–Physical Systems Perspective. International Research Journal of Engineering and Technology. [43] Khazaei, Peyman, et al. ”Applying the modified TLBO algorithm to solve the unit commitment problem.” In 2016 World Automation Congress (WAC), pp. 1-6. IEEE, 2016. [44] Kavousi-Fard, Abdollah, et al. ”Artificial intelligence and blockchain-enabled decentralized electric vehicle charging management.” Journal of Electrical Engineering & Technology (2023). [45] Al-Mhiqani, M. N., Alsboui, T., Al-Shehari, T., & others. (2024). Insider Threat Detection in Cyber-Physical Systems: A Systematic Literature Review. Computers and Security. [46] Tajmasebi, Dorna, et al. ”A secure distributed cloud-fog based framework for economic operation of microgrids.” In 2019 IEEE Texas Power and Energy Conference (TPEC), pp. 1-6. IEEE, 2019. [47] Aktan, E., Bartoli, I., Gliˇsi´c, B., & Rainieri, C. (2024). Lessons from Bridge Structural Health Monitoring (SHM) and Their Implications for the Development of Cyber-Physical Systems. Infrastructures. [48] Tajmasebi, Dorna, et al. ”A secure distributed cloud-fog based framework for economic operation of microgrids.” In 2019 IEEE Texas Power and Energy Conference (TPEC), pp. 1-6. IEEE, 2019. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/123270 |