Construction of customer-aware grid interactive service system based on neural network model

[1] Jovanovi, J. A., Vujadinovi, R., Mitreva, E., et al. (2020). The Relationship between E-Commerce and Firm Performance: The Mediating Role of Internet Sales Channels. Sustainability, 12. Search in Google Scholar

[2] Geert, Litjens, Thijs, et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis. Search in Google Scholar

[3] Voets, M., Mllersen, K., Bongo, L. A. (2018). Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PLoS ONE. Search in Google Scholar

[4] Peng, X., Li, Q., Jing, J. (2022). CNGAT: A Graph Neural Network Model for Radar Quantitative Precipitation Estimation. IEEE Transactions on Geoscience and Remote Sensing, 60. Search in Google Scholar

[5] Polat, M. E., Cadirci, S. (2022). Artificial neural network model and multi-objective optimization of microchannel heat sinks with diamond-shaped pin fins. International Journal of Heat and Mass Transfer, (Pt.1), 194. Search in Google Scholar

[6] Jansen, J., Drenthen, G. S. (2022). Editorial for “MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma”. Journal of magnetic resonance imaging: JMRI, (2), 56. Search in Google Scholar

[7] Liu, P., Shi, R., Meng, F., et al. (2022). Combining Multi-Indices by Neural Network Model for Estimating Canopy Chlorophyll Content: a Case Study of Interspecies Competition between Spartina alterniflora and Phragmites australis. Polish Journal of Environmental Studies. (1 Pt.1), 31. Search in Google Scholar

[8] Rahbari-Asr, N., Ojha, U., Zhang, Z., et al. (2017). Incremental Welfare Consensus Algorithm for Cooperative Distributed Generation/Demand Response in Smart Grid. IEEE Transactions on Smart Grid, 5(6), 2836-2845. Search in Google Scholar

[9] Bharathi, C., Rekha, D., Vijayakumar, V. (2017). Genetic Algorithm Based Demand Side Management for Smart Grid. Wireless Personal Communications, 93(2), 481-502. Search in Google Scholar

[10] Good, N., Ellis, K. A., Mancarella, P. (2017). Review and classification of barriers and enablers of demand response in the smart grid. Renewable & Sustainable Energy Reviews, 72.57-72. Search in Google Scholar

[11] Collier, Steven, E. (2017). The Emerging Enernet: Convergence of the Smart Grid with the Internet of Things. IEEE Industry Applications Magazine, 23(2), 12-16. Search in Google Scholar

[12] Yolda, Y., Nen, A., Muyeen, S. M., et al. (2017). Enhancing smart grid with microgrids: Challenges and opportunities. Renewable and Sustainable Energy Reviews, 72,205-214. Search in Google Scholar

[13] Julius, J., Yang, X., Han, W. (2017). A survey of intrusion detection systems in smart grid. International Journal of Sensor Networks, 23(3), 170-186. Search in Google Scholar

[14] Karthikeyan, A., Rajagopal, K. (2017). Chaos Control in Fractional Order Smart Grid with Adaptive Sliding Mode Control and Genetically Optimized PID Control and Its FPGA Implementation. Complexity, 1-18. Search in Google Scholar

[15] Du, X., Qi, Y., Chen, B., Shan, B., & Liu, X. (2021). The integration of blockchain technology and smart grid: framework and application. Mathematical Problems in Engineering. Search in Google Scholar

[16] Markus, M., Lukas, R., Jan, D., et al. (2018). A Cosimulation Architecture for Power System, Communication, and Market in the Smart Grid. Complexity, 1-12. Search in Google Scholar

[17] Paulo, D. A., Raimir, F., Joel, R., et al. (2018). Infrastructure for Integration of Legacy Electrical Equipment into a Smart-Grid Using Wireless Sensor Networks. Sensors, 18(5), 1312. Search in Google Scholar

[18] Srisomboon, K., Dindam, T., Lee, W. (2021). Empowered Hybrid Parent Selection for Improving Network Lifetime, PDR, and Latency in Smart Grid. Mathematical Problems in Engineering. Search in Google Scholar