Research on Data-Driven Dynamic Reconfiguration and Energy-Saving Optimization Methods for Building Energy-Consuming Microgrids
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Abstract
This study proposes a data-driven dynamic reconfiguration and energy-saving optimization framework for building energy-consuming microgrids. By integrating multi-source sensing data, load forecasting, and dynamic topology optimization, the framework establishes a coupling mechanism between building energy consumption and microgrid operation. A hierarchical architecture consisting of data acquisition, intelligent prediction, optimization decisionmaking, and execution control is developed to support adaptive energy management under varying load conditions. Load forecasting is performed using a long short-term memory network, while an enhanced particle swarm optimization algorithm is employed to determine optimal microgrid reconfiguration strategies under operational constraints. Case studies based on commercial building scenarios demonstrate that the proposed framework effectively improves load regulation capability, enhances renewable energy utilization, and supports stable microgrid operation. The framework can be readily integrated with wireless sensing infrastructures, antenna-enabled monitoring platforms, and edge–cloud collaborative systems, providing an engineering-oriented solution for real-time energy management and intelligent microgrid optimization in modern building environments.
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References
M. Alharbi and A. S. Alghamdi, “Hybrid CNN-GRU Forecasting and Improved Teaching–Learning-Based Optimization for Cost-Efficient Microgrid Energy Management,” Processes, vol. 13, no. 5, p. 1452, 2025, doi: 10.3390/pr13051452.
Y. Liu and F. Wan, “Integrated Optimization of Microgrids with Renewable Energy, Electric Vehicles, and Adaptive Demand Response for Sustainable and Efficient Energy Management,” Smart Grids and Sustainable Energy, vol. 10, no. 1, pp. 1–22, 2025, doi: 10.1007/s40866-025-00257-1.
G. Lv, Y. Ji, Y. Zhang, W. Wang, J. Zhang, J. Chen, et al., “Optimization of building microgrid energy system based on virtual energy storage,” Frontiers in Energy Research, vol. 10, p. 1053498, 2023, doi: 10.3389/fenrg.2022.1053498.
X. Guan, Z. Xu, and Q. S. Jia, “Energy-efficient buildings facilitated by microgrid,” IEEE Transactions on Smart Grid, vol. 1, no. 3, pp. 243–252, 2010, doi: 10.1109/TSG.2010.2083705.
M. Kermani, B. Adelmanesh, E. Shirdare, C. A. Sima, D. L. Carnì, and L. Martirano, “Intelligent energy management based on SCADA system in a real Microgrid for smart building applications,” Renewable Energy, vol. 171, pp. 1115–1127, 2021, doi: 10.1016/j.renene.2021.03.008.
Z. Xu, X. Guan, Q. S. Jia, J. Wu, D. Wang, and S. Chen, “Performance Analysis and Comparison on Energy Storage Devices for Smart Building Energy Management,” IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 2136–2147, 2012, doi: 10.1109/TSG.2012.2218836.
S. Zheng, S. Fu, Y. Pu, D. Li, M. Arıcı, D. Wang, et al., “Energy-saving microgrid system for underground in-situ heating of oil shale integrating renewable energy source: An analysis focusing on net present value,” Journal of the Taiwan Institute of Chemical Engineers, vol. 148, p. 104717, 2023, doi: 10.1016/j.jtice.2023.104717.
M. M. Kamal and I. Ashraf, “Planning and Optimization of Hybrid Microgrid for Reliable Electrification of Rural Region,” Journal of The Institution of Engineers (India), Series B: Electrical Engineering, Electronics and Telecommunication Engineering, Computer Engineering, vol. 103, no. 1, pp. 173–188, 2022, doi: 10.1007/s40031-021-00631-4.
F. Moazeni and J. Khazaei, “Dynamic economic dispatch of islanded water-energy microgrids with smart building thermal energy management system,” Applied Energy, vol. 276, p. 115422, 2020, doi: 10.1016/j.apenergy.2020.115422.
Y. Zhang, Z. Yan, F. Yuan, J. Yao, and B. Ding, “A Novel Reconstruction Approach to Elevator Energy Conservation Based on a DC Micro-Grid in High-Rise Buildings,” Energies, vol. 12, no. 1, p. 33, 2018, doi: 10.3390/en12010033.
F. Jabari, H. Arasteh, A. Sheikhi-Fini, and B. Mohammadi-Ivatloo, “Optimization of a Tidal-Battery-Diesel Driven Energy-Efficient Standalone Microgrid Considering the Load-Curve Flattening Program,” International Transactions on Electrical Energy Systems, vol. 31, no. 9, p. e12993, 2021, doi: 10.1002/2050-7038.12993.
N. Liu, X. Yu, C. Wang, C. Li, L. Ma, and J. Lei, “Energy-Sharing Model With Price-Based Demand Response for Microgrids of Peer-to-Peer Prosumers,” IEEE Transactions on Power Systems, vol. 32, no. 5, pp. 3569–3583, 2017, doi: 10.1109/TPWRS.2017.2649558.
Q. S. Jia, J. X. Shen, Z. B. Xu, and X. H. Guan, “Simulation-Based Policy Improvement for Energy Management in Commercial Office Buildings,” IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 2211–2223, 2012, doi: 10.1109/TSG.2012.2214069.
P. Wei and W. Chen, “Microgrid in China: A review in the perspective of application,” Energy Procedia, vol. 158, pp. 6601–6606, 2019, doi: 10.1016/j.egypro.2019.01.059.
A. Mohamed, V. Salehi, and O. Mohammed, “Real-Time Energy Management Algorithm for Mitigation of Pulse Loads in Hybrid Microgrids,” IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 1911–1922, 2012, doi: 10.1109/TSG.2012.2200702.
H. Wu, G. Cao, R. Jia, and Y. Liang, “Co-Optimization Operation of Distribution Network-Containing Shared Energy Storage Multi-Microgrids Based on Multi-Body Game,” Sensors, vol. 25, no. 2, p. 406, 2025, doi: 10.3390/s25020406.
M. Vicente, A. Imperadore, F. X. Correia da Fonseca, M. Vieira, and J. Cândido, “Enhancing Islanded Power Systems: Microgrid Modeling and Evaluating System Benefits of Ocean Renewable Energy Integration,” Energies, vol. 16, no. 22, p. 7517, 2023, doi: 10.3390/en16227517.
S. Jamal, N. M. L. Tan, and J. Pasupuleti, “A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications,” Sustainability, vol. 13, no. 18, p. 10331, 2021, doi: 10.3390/su131810331.
L. Yan, X. Chen, and Y. Chen, “A consensus-based privacy-preserving energy management strategy for microgrids with event-triggered scheme,” International Journal of Electrical Power and Energy Systems, vol. 141, p. 108198, 2022, doi: 10.1016/j.ijepes.2022.108198.
M. F. Roslan, M. A. Hannan, P. J. Ker, R. A. Begum, T. I. Mahlia, and Z. Y. Dong, “Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction,” Applied Energy, vol. 292, p. 116883, 2021, doi: 10.1016/j.apenergy.2021.116883.
X. Lü, Y. Wu, J. Lian, et al., “Energy management and optimization of PEMFC/battery mobile robot based on hybrid rule strategy and AMPSO,” Renewable Energy, vol. 171, 2021, doi: 10.1016/j.renene.2021.02.135.
C. Keles, B. B. Alagoz, A. Kaygusuz, and S. Alagoz, “Cost-Efficient Multi-Source Energy Mixing for Renewable Energy Microgrids via Random Search Optimization,” International Artificial Intelligence and Data Processing Symposium; 17–18 Sep 2016; Malatya, Turkey, 2016, doi: 10.15308/sinteza-2021-39-45.
L. Ding, Y. Men, J. Zhang, X. Lu, J. Tan, and Y. Cao, “Grid-Forming Inverters for Power System Resilience Enhancement: Modeling, Control, and Case Studies for Dynamic Microgrids in Distribution Systems and Hybrid Power Plants in Transmission Grids,” Power Electronics and Power Systems, pp. 367–396, 2025, doi: 10.1007/978-3-031-73978-1_8.
Z. Zhang, Y. Huang, Q. Huang, and W. J. Lee, “A Novel Hierarchical Demand Response Strategy for Residential Microgrid with Time-Varying Price,” in Proc. 2020 IEEE Industry Applications Society Annual Meeting, Detroit, MI, USA, 10–16 October 2020, doi: 10.1109/IAS44978.2020.9334895.
F. A. Sarwar, I. Hernando-Gil, and I. Vechiu, “Review of energy management systems and optimization methods for hydrogen-based hybrid building microgrids,” Energy Conversion and Economics, vol. 5, no. 4, pp. 259–279, 2024, doi: 10.1049/enc2.12126.