Deep Learning-Enabled Holistic Control and Prediction System for Building Energy Consumption and Distribution Optimization

References

• Z. Jia, Y. Tian, Z. Liu and S. Fan, “Condition assessment of the cable trench based on an intelligent inspection robot,” Front. Energy Res., vol. 10, pp. 860461, 2022. DOI: 10.3389/fenrg.2022.860461.
   Web of Science ®Google Scholar
• W. Chen, D. Luo, F. Fu, H. He and K. Zhang, “Research on the transformer intelligent operation and maintenance system based on the graph neural network,” Front. Energy Res., vol. 10, pp. 935359, 2022. DOI: 10.3389/fenrg.2022.935359.
   Web of Science ®Google Scholar
• Y. Wu, C. Zhang and W. Liu, “Living tree moisture content detection method based on intelligent UHF RFID Sensors and OS-PELM,” Sensors, vol. 22, no. 16, pp. 6287, 2022. DOI: 10.3390/s22166287.
   PubMed Web of Science ®Google Scholar
• Y. Zhang, Q. Lai, J. Wang and C. Lü, “Piezoelectric energy harvesting from roadways under open-traffic conditions: analysis and optimization with scaling law method,” Energies, vol. 15, no. 9, pp. 3395, 2022. DOI: 10.3390/en15093395.
   Web of Science ®Google Scholar
• G. Da-Wei, Q. Zhi-Qiang, Z. Wei, K. Zhi-Wu and L. Yang, “Intelligent decoupling control study of PMSM based on the neural network inverse system,” Front. Energy Res., vol. 10, pp. 936776, 2022. DOI: 10.3389/fenrg.2022.936776.
   Web of Science ®Google Scholar
• B. Wen, H. Li, J. Zhang, Q. Han and Z. Ding, “An intelligent analysis method of security and stability control strategy based on the knowledge graph,” Front. Energy Res., vol. 10, pp. 1022231, 2023. DOI: 10.3389/fenrg.2022.1022231.
   Web of Science ®Google Scholar
• A. M. Jasim, B. H. Jasim, B.-C. Neagu and S. Attila, “Electric vehicle battery-connected parallel distribution generators for intelligent demand management in smart microgrids,” Energies, vol. 16, no. 6, pp. 2570, 2023. DOI: 10.3390/en16062570.
   Web of Science ®Google Scholar
• E. García, E. Quiles and A. Correcher, “Distributed intelligent battery management system using a real-world cloud computing system,” Sensors, vol. 23, no. 7, pp. 3417, 2023. DOI: 10.3390/s23073417.
   PubMed Web of Science ®Google Scholar
• S. Huang, X. Gao, J. Chen, R. Chen, Z. Su and J. Bao, “An optimal hierarchical pricing strategy for shared energy storage services,” Front. Energy Res., vol. 10, pp. 967998, 2022. DOI: 10.3389/fenrg.2022.967998.
   Web of Science ®Google Scholar
• W. Lin, et al., “Predictive energy management strategy for range-extended electric vehicles based on ITS information and start–stop optimization with vehicle velocity forecast,” Energies, vol. 15, no. 20, pp. 7774, 2022. DOI: 10.3390/en15207774.
   Web of Science ®Google Scholar
• J. Zhang and H. Wei, “A review on configuration optimization of hybrid energy system based on renewable energy,” Front. Energy Res., vol. 10, pp. 977925, 2022. DOI: 10.3389/fenrg.2022.977925.
   Web of Science ®Google Scholar
• S. S. Tripathy, M. Rath, N. Tripathy, D. S. Roy, J. S. A. Francis and S. Bebortta, “An intelligent health care system in fog platform with optimized performance,” Sustainability, vol. 15, no. 3, pp. 1862, 2023. DOI: 10.3390/su15031862.
   Web of Science ®Google Scholar
• J. Han, X. Zhou, W. Zhang, Q. Guo, J. Wang and Y. Lu, “Directed representative graph modeling of MEP systems using BIM data,” Buildings, vol. 12, no. 6, pp. 834, 2022. DOI: 10.3390/buildings12060834.
   Web of Science ®Google Scholar
• L. Pekař, M. Song, N. Mao, H. M. Ali, W. Wu and J. Ca, “Emerging sustainable and energy-efficient technologies in heat pumps for residential heating,” Front. Energy Res., vol. 10, pp. 866466, 2022. DOI: 10.3389/fenrg.2022.866466.
   Web of Science ®Google Scholar
• H. Moayedi and A. Mosavi, “Double-target based neural networks in predicting energy consumption in residential buildings,” Energies, vol. 14, no. 5, pp. 1331, 2021. DOI: 10.3390/en14051331.
   Web of Science ®Google Scholar
• A. Sadeghi, R. Younes Sinaki, W. A. Young and G. R. Weckman, “An intelligent model to predict energy performances of residential buildings based on deep neural networks,” Energies, vol. 13, no. 3, pp. 571, 2020. DOI: 10.3390/en13030571.
   Web of Science ®Google Scholar
• D. Rubinetti, D. A. Weiss, D. Kraniotis and A. Chaudhuri, “Fully integrated numerical design tool to assess the thermal performance of phase change materials (PCM) in the built environment,” IOP Conf. Ser. Earth Environ. Sci., vol. 410, no. 1, pp. 012114, 2020. DOI: 10.1088/1755-1315/410/1/012114.
   Google Scholar
• K. Koupidis, C. Bratsas and C. Vlachokostas, “OpΕnergy: an intelligent system for monitoring EU energy strategy using EU Open Data,” Energies, vol. 15, no. 21, pp. 8294, 2022. DOI: 10.3390/en15218294.
   Web of Science ®Google Scholar
• J. Liang, B. Chen, C. Shao, J. Li and B. Wu, “Time reverse modeling of damage detection in underwater concrete beams using piezoelectric intelligent modules,” Sensors, vol. 20, no. 24, pp. 7318, 2020. DOI: 10.3390/s20247318.
   PubMed Web of Science ®Google Scholar
• E. Barakhtenko and D. Sokolov, “A methodological approach to the development of a technological platform for intelligent integrated energy system modeling,” In E3S Web Conf., vol. 219, pp. 04003, 2020. DOI: 10.1051/e3sconf/202021904003.
   Google Scholar
• P. Zhou, M. Han and Y. Shen, “Impact of intelligent manufacturing on total-factor energy efficiency: mechanism and improvement path,” Sustainability, vol. 15, no. 5, pp. 3944, 2023. DOI: 10.3390/su15053944.
   Web of Science ®Google Scholar
• W. Zhang and X. Lin, “Research on intelligent regulation system of solar panels driven by low-voltage electric energy,” In E3S Web Conf., vol. 375, pp. 02018, 2023. DOI: 10.1051/e3sconf/202337502018.
   Google Scholar
• X. Rui, et al., “An intelligent self-powered pipeline inner spherical detector with piezoelectric energy harvesting,” IEEE Access, vol. 7, pp. 104621–104629, 2019. DOI: 10.1109/ACCESS.2019.2932459.
   Web of Science ®Google Scholar
• S. Liu, B. Zhao, S. Zhao, L. Zhang and L. Wu, “An intelligent bio-inspired cooperative decoupling control strategy for the marine boiler-turbine system with a novel energy dynamic model,” Energies, vol. 12, no. 24, pp. 4659, 2019. DOI: 10.3390/en12244659.
   Web of Science ®Google Scholar
• Y. Zhao, N. Wang, Z. Liu and E. Mu, “Construction theory for a building intelligent operation and maintenance system based on digital twins and machine learning,” Buildings, vol. 12, no. 2, pp. 87, 2022. DOI: 10.3390/buildings12020087.
   Web of Science ®Google Scholar
• S. Baek, et al., “Intelligent feature selection for ECG-based personal authentication using deep reinforcement learning,” Sensors, vol. 23, no. 3, pp. 1230, 2023. DOI: 10.3390/s23031230.
   PubMed Web of Science ®Google Scholar
• E. Y. Obzherin, S. M. Sidorov and M. M. Nikitin, “On application of semi-Markov processes superposition to energy systems modeling,” E3S Web Conf., vol. 139, pp. 01064, 2019. DOI: 10.1051/e3sconf/201913901064.
   Google Scholar
• E. Barakhtenko, D. Sokolov and V. Tashlykova, “Modeling of a prosumer in optimal design energy systems problem,” E3S Web Conf., vol. 102, pp. 02001–EDP Sciences, 2019. DOI: 10.1051/e3sconf/201910202001.
   Google Scholar
• W. Zhao, J. Zhao, X. Yao, Z. Jin and P. Wang, “A novel adaptive intelligent ensemble model for forecasting primary energy demand,” Energies, vol. 12, no. 7, pp. 1347, 2019. DOI: 10.3390/en12071347.
   Web of Science ®Google Scholar
• Y. Zeng, “Research on intelligent multimedia distance teaching system considering virtual reality technology,” Math. Prob. Engng., vol. 2022, pp. 1–12, 2019. DOI: 10.1155/2022/1811436.
   Google Scholar
• A. Rajaram and K. Sathiyaraj, “An improved optimization technique for energy harvesting system with grid connected power for green house management,” J. Electr. Eng. Technol., vol. 17, no. 5, pp. 2937–2949, 2022. DOI: 10.1007/s42835-022-01033-2.
   Web of Science ®Google Scholar
• P. Ashok Babu, J. L. Mazher Iqbal, S. Siva Priyanka, M. Jithender Reddy, G. Sunil Kumar and R. Ayyasamy, “Power control and optimization for power loss reduction using deep learning in microgrid systems,” Electric Power Components Syst., vol. 52, no. 2, pp. 219–232, 2024. DOI: 10.1080/15325008.2023.2217175.
   Web of Science ®Google Scholar
• H. Shekhar, et al., “Demand side control for energy saving in renewable energy resources using deep learning optimization,” Electric Power Components Syst., vol. 51, no. 19, pp. 2397–2413, 2023. DOI: 10.1080/15325008.2023.2246463.
   Web of Science ®Google Scholar
• M. N. Ali, M. Amer and M. Elsisi, “Reliable IoT paradigm with ensemble machine learning for faults diagnosis of power transformers considering adversarial attacks,” IEEE Trans. Instrum. Meas., vol. 72, pp. 1–13, 2023. DOI: 10.1109/TIM.2023.3300444.
   PubMed Web of Science ®Google Scholar
• M. Elsisi, C. L. Su and M. N. Ali, “Design of reliable IoT systems with deep learning to support resilient demand side management in smart grids against adversarial attacks,” IEEE Trans. Ind. Appl., vol. 60, no. 2, pp. 2095–2106, 2024. DOI: 10.1109/TIA.2023.3297089.
   Google Scholar
• W. S. Cheng, G. Y. Chen, X. Y. Shih, M. Elsisi, M. H. Tsai and H. J. Dai, “Vickers hardness value test via multi-task learning convolutional neural networks and image augmentation,” Appl. Sci., vol. 12, no. 21, pp. 10820, 2022. DOI: 10.3390/app122110820.
   Google Scholar
• M. Elsisi, M. Amer, A. Dababat and C.-L. Su, “A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation,” Energy, vol. 281, pp. 128256, 2023. DOI: 10.1016/j.energy.2023.128256.
   Web of Science ®Google Scholar
• G. Sakkarvarthi, G. W. Sathianesan, V. S. Murugan, A. J. Reddy, P. Jayagopal and M. Elsisi, “Detection and classification of tomato crop disease using convolutional neural network,” Electronics, vol. 11, no. 21, pp. 3618, 2022. DOI: 10.3390/electronics11213618.
   Web of Science ®Google Scholar
• M. Tran, et al., “Robust fault recognition and correction scheme for induction motors using an effective IoT with deep learning approach,” Measurement, vol. 207, pp. 112398, 2023. DOI: 10.1016/j.measurement.2022.112398.
   Web of Science ®Google Scholar
• Z. Chang, Y. Zhang and W. Chen, “Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform,” Energy, vol. 187, pp. 115804, 2019. DOI: 10.1016/j.energy.2019.07.134.
   Web of Science ®Google Scholar
• Y. Ding, Y. Zhu, J. Feng, P. Zhang and Z. Cheng, “Interpretable spatio-temporal attention LSTM model for flood forecasting,” Neurocomputing, vol. 403, pp. 348–359, 2020. DOI: 10.1016/j.neucom.2020.04.110.
   Web of Science ®Google Scholar
• S. R. Moreno, R. G. da Silva, V. C. Mariani and L. dos Santos Coelho, “Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network,” Energy Conversion Manage, vol. 213, pp. 112869, 2020. DOI: 10.1016/j.enconman.2020.112869.
   Web of Science ®Google Scholar
• Y. S. Chang, H. T. Chiao, S. Abimannan, Y. P. Huang, Y. T. Tsai and K. M. Lin, “An LSTM-based aggregated model for air pollution forecasting,” Atmos. Poll. Res., vol. 11, no. 8, pp. 1451–1463, 2020. DOI: 10.1016/j.apr.2020.05.015.
   Web of Science ®Google Scholar