Construction of a smart grid load forecasting platform based on clustering algorithm

[1] Huang, D., Liu, H., Bi, T., et al. (2022). GCN-LSTM Spatiotemporal-Network-Based Method for Post-Disturbance Frequency Prediction of Power Systems. Global Energy Internet, 5(1), 12. Search in Google Scholar

[2] Chen, Y., & Zhao, Z. (2018). Study on the Improved Interpretive Structural Modeling of the Factors Influence the Development of Regional Renewable Energy Power Generation Projects. Power System and Clean Energy. Search in Google Scholar

[3] Sarhan, M. A., A Bień, Barczentewicz, S., et al. (2021). A Review of Electricity and Renewable Energy Sectors Status and Prospect in Jordan. Przeglad Elektrotechniczny, 97(9). Search in Google Scholar

[4] Monfaredi, F., Shayeghi, H., & Bizon, N. (2022). Developing optimal energy management of integrated energy systems in the hybrid electricity and gas networks. Journal of Energy Storage, 48, 103984. Search in Google Scholar

[5] Basak, S., Dey, B., & Bhattacharyya, B. (2022). Demand Side Management for Solving Environment Constrained Economic Dispatch of a Microgrid System Using Hybrid MGWOSCACSA Algorithm. Journal of Intelligent Technology, 7(2), 12. Search in Google Scholar

[6] De, M., Das, G., & Mandal, K. K. (2021). An Effective Energy Flow Management in Grid-Connected Solar-Wind-Microgrid System Incorporating Economic and Environmental Generation Scheduling Using a Meta-Dynamic Approach-Based Multiobjective Flower Pollination Algorithm. Energy Reports. Search in Google Scholar

[7] Zhou, X., Chen, S., Lu, Z., Huang, Y., Ma, S., & Zhao, Q. (2018). Technology features of the new generation power system in China. Proceedings of the CSEE, 38(7), 1893-1904. Search in Google Scholar

[8] Mel, I. D., Klymenko, O. V., & Short, M. (2021). Optimal Design of Distributed Energy Systems Considering the Impacts on Electrical Power Networks. Search in Google Scholar

[9] Jae-Chul, Kim, Sung-Min, et al. (2016). Advanced Power Distribution System Configuration for Smart Grid. Smart Grid IEEE Transactions on. Search in Google Scholar

[10] Liu, Y., Guo, W., Fan, C. I., Chang, L., & Cheng, C. (2018). A practical privacy-preserving data aggregation (3PDA) scheme for smart grid. IEEE Transactions on Industrial Informatics, 15(3), 1767-1774. Search in Google Scholar

[11] Passerini, F., & Tonello, A. M. (2019). Smart grid monitoring using power line modems: Effect of anomalies on signal propagation. IEEE Access, 7, 27302-27312. Search in Google Scholar

[12] Zhou, H., Chen, J., Zhang, Y. Y., et al. (2017). A Multidimensional Data Aggregation Scheme in Multilevel Network in Smart Grid. Journal of Cryptologic Research, 4, 114-132. Search in Google Scholar

[13] Zhang, B. H., Zhao, J. J., Liu, X. H., et al. (2004). Short-Term Load Forecasting Based on Wavelet Neural Network. Power System Technology. Search in Google Scholar

[14] Khwaja, A. S., Naeem, M., Anpalagan, A., et al. (2015). Improved Short-Term Load Forecasting Using Bagged Neural Networks. Electric Power Systems Research, 125, 109-115. Search in Google Scholar

[15] Raza, M. Q., Nadarajah, M., Duong, Q. H., et al. (2017). An Intelligent Hybrid Short-Term Load Forecasting Model for Smart Power Grids. Sustainable Cities and Society, 31, 264-275. Search in Google Scholar

[16] Ahmad, A., Javaid, N., Mateen, A., et al. (2019). Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach. Energies, 12(1641). Search in Google Scholar

[17] Shehu, G. S., Cetinkaya, N. (2019). Flower Pollination-Feedforward Neural Network for Load Flow Forecasting in Smart Distribution Grid. Neural Computing & Applications, 31(10), 6001-6012. Search in Google Scholar

[18] Lopez, S. M., Valero, C., Senabre, C., et al. (2012). Application of SOM Neural Networks to Short-Term Load Forecasting: The Spanish Electricity Market Case Study. Electric Power Systems Research, 91, 18-27. Search in Google Scholar

[19] Lahouar, A., Slama, J. B. H. (2015). Day-Ahead Load Forecast Using Random Forest and Expert Input Selection. Energy Conversion & Management, 103, 1040-1051. Search in Google Scholar

[20] Taylor, J. W., McSharry, P. E. (2017). Short-Term Load Forecasting Methods: An Evaluation Based on European Data. IEEE Transactions on Power Systems, 22(4), 2213-2219. Search in Google Scholar

[21] Bala, I., Supervisor, A. (2014). Fuzzy Logic Dynamic Electricity Price Forecasting Model for Smart Grid. Sudan University of Science & Technology. Search in Google Scholar

[22] Kaseng, F., Lezama, P., Inquilla, R., et al. (2020). Evolution and Advance Usage of Internet in Peru. 3c TIC: Cuadernos de Desarrollo Aplicados a las TIC, 9(4), 113-127. Search in Google Scholar

[23] Dewani, A., Memon, M. A., Bhatti, S. (2021). Development of Computational Linguistic Resources for Automated Detection of Textual Cyberbullying Threats in Roman Urdu Language. 3c TIC: Cuadernos de Desarrollo Aplicados a las TIC, 10(2), 101-121. Search in Google Scholar