مدیریت انرژی ساختمان‌های هوشمند با استفاده از شبکه‌های عصبی گرافی

[1] Available online at: https://www.iea.org/energy-system/buildings

[2] M. Khalil, A. S. McGough, Z. Pourmirza, M. Pazhoohesh, and S. Walker, "Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption—A systematic review," Engineering Applications of Artificial Intelligence, vol. 115, p. 105287, 2022.

[3] L. Zhang et al., "A review of machine learning in building load prediction," Applied Energy, vol. 285, p. 116452, 2021.

[4] Y. Hu et al., "A data-driven approach for the disaggregation of building-sector heating and cooling loads from hourly utility load data," Energy Strategy Reviews, vol. 49, p. 101175, 2023.

[5] S. Afzal, A. Shokri, B. M. Ziapour, H. Shakibi, and B. Sobhani, "Building energy consumption prediction and optimization using different neural network-assisted models; comparison of different networks and optimization algorithms," Engineering Applications of Artificial Intelligence, vol. 127, p. 107356, 2024.

[6] Y. Li, Z. O'Neill, L. Zhang, J. Chen, P. Im, and J. DeGraw, "Grey-box modeling and application for building energy simulations-A critical review," Renewable and Sustainable Energy Reviews, vol. 146, p. 111174, 2021.

[7] W. Cai, X. Wen, C. Li, J. Shao, and J. Xu, "Predicting the energy consumption in buildings using the optimized support vector regression model," Energy, vol. 273, p. 127188, 2023.

[8] M. Peplinski, B. Dilkina, M. Chen, S. J. Silva, G. A. Ban-Weiss, and K. T. Sanders, "A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets," Applied Energy, vol. 357, p. 122413, 2024.

[9] A. Li, F. Xiao, C. Zhang, and C. Fan, "Attention-based interpretable neural network for building cooling load prediction," Applied Energy, vol. 299, p. 117238, 2021.

[10] M. Rajabimandi, M. E. Hajiabadi, Majid Baghainejad, "A Hybrid Algorithm Based on Computational Intelligence Methods for Managing Home Electricity Consumption with the Presence of Electric Vehicles", Journal of Electrical Engineering, University of Tabriz, vol. 48, Issue 2, pp. 617-629, 2018.

[11] A. Samadi, H. Saeedi, M. A. Latifi, M. Mahdavi, "Home Consumption Management Considering the Effect of Environmental Control Demands on Limited Time Demands", Journal of Electrical Engineering, University of Tabriz, vol. 50, Issue 2, pp. 765-776, 2019.

[12] C. Chellaswamy, R. Ganesh Babu, and A. Vanathi, "A framework for building energy management system with residence mounted photovoltaic," in Building Simulation, vol. 14, no. 4: Springer, pp. 1031-1046, 2021.

[13] W. Liao, B. Bak-Jensen, J. R. Pillai, Z. Yang, and K. Liu, "Short-term power prediction for renewable energy using hybrid graph convolutional network and long short-term memory approach," Electric Power Systems Research, vol. 211, p. 108614, 2022.

[14] S. Arastehfar, M. Matinkia, and M. R. Jabbarpour, "Short-term residential load forecasting using graph convolutional recurrent neural networks," Engineering Applications of Artificial Intelligence, vol. 116, p. 105358, 2022.

[15] J. Lu et al., "Graph convolutional networks-based method for estimating design loads of complex buildings in the preliminary design stage," Applied Energy, vol. 322, p. 119478, 2022.

[16] P. Veličković, "Everything is connected: Graph neural networks," Current Opinion in Structural Biology, vol. 79, p. 102538, 2023.

[17] N. A. Asif et al., "Graph neural network: A comprehensive review on non-euclidean space," IEEE Access, vol. 9, pp. 60588-60606, 2021.

[18] Y. Wu, H.-N. Dai, and H. Tang, "Graph neural networks for anomaly detection in industrial internet of things," IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9214-9231, 2021.

[19] W. Liao, B. Bak-Jensen, J. R. Pillai, Y. Wang, and Y. Wang, "A review of graph neural networks and their applications in power systems," Journal of Modern Power Systems and Clean Energy, vol. 10, no. 2, pp. 345-360, 2021.

[20] T. N. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks," arXiv preprint arXiv:1609.02907, 2016.

[21] Available online at: https://pytorch-geometric.readthedocs.io/en/ latest/generated/torch_geo_geometric.nn.conv.GCNConv.html#torch_geometric.nn.conv.GCNConv

[22] Available online at: https://pytorch-geometric.readthedocs.io/en/

latest/generated/torch_geometric.nn.pool.global_max_pool.html#torch_geometric.nn.pool.global_max_pool

[23] M. Pipattanasomporn et al., "CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets," Scientific Data, vol. 7, no. 1, p. 241, 2020.