Research on the development of AloT sensing module for new energy power information collection

[1] Vlker, B., Pfeifer, M., Scholl, P. M., & Becker, B. (2020). A framework to generate and label datasets for non-intrusive load monitoring. Energies, 14. Search in Google Scholar

[2] Zhang, W., Dong, X., Li, H., Xu, J., & Wang, D. (2020). Unsupervised detection of abnormal electricity consumption behavior based on feature engineering. IEEE Access, 8, 55483-55500. Search in Google Scholar

[3] D Romero-Jordán, & PD Río. (2022). Analysing the drivers of the efficiency of households in electricity consumption. Energy Policy, 164, 112828-. Search in Google Scholar

[4] Cui, G., Liu, B., & Luan, W. (2019). Neural network with extended input for estimating electricity consumption using background-based data generation. ENERGY PROCEDIA. 158:2683-3688. Search in Google Scholar

[5] Khanna, N. Z., Guo, J., & Zheng, X. (2016). Effects of demand side management on chinese household electricity consumption: empirical findings from chinese household survey. Energy Policy, 95, 113-125. Search in Google Scholar

[6] Ramos, D., Faria, P., Vale, Z., Mourinho, J., & Correia, R. (2020). Industrial facility electricity consumption forecast using artificial neural networks and incremental learning. Energies, 13(18), 4774. Search in Google Scholar

[7] Song, W., Feng, N., Tian, Y., Fong, S., & Cho, K. (2018). A deep belief network for electricity utilisation feature analysis of air conditioners using a smart iot platform. Journal of Information Processing Systems, 14(1), 162-175. Search in Google Scholar

[8] Alexis, Gerossier, Thibaut, Barbier, Robin, & Girard. (2017). A novel method for decomposing electricity feeder load into elementary profiles from customer information. Applied Energy. 203:752-760. Search in Google Scholar

[9] Popovi, I., Raki, A., & Petruevski, I. D. (2022). Multi-agent real-time advanced metering infrastructure based on fog computing. Energies, 15(1), 373-. Search in Google Scholar

[10] Afzaal, A., Kanwal, F., Ali, A. H., Bashir, K., & Anjum, F. (2020). Agent-based energy consumption scheduling for smart grids: an auction-theoretic approach. IEEE Access, 8(99), 73780-73790. Search in Google Scholar

[11] Joseph, A., & Balachandra, P. (2020). Smart grid to energy internet: a systematic review of transitioning electricity systems. IEEE Access, 8, 215787-215805. Search in Google Scholar

[12] Chang, C. J., Lin, J. Y., & Chang, M. J. (2016). Extended modeling procedure based on the projected sample for forecasting short-term electricity consumption. Advanced Engineering Informatics, 30(2), 211-217. Search in Google Scholar

[13] Elif, U. S., Leyli, K., Zeki, B., Emrah, T., Mehmet, A. E., & Ferhat, K., et al. (2021). Context-aware authentication with dynamic credentials using electricity consumption data. The Computer Journal(10), 10. Search in Google Scholar

[14] Li, S., Yang, J., Song, W. Z., & Chen, A. (2018). A real-time electricity scheduling for residential home energy management. IEEE Internet of Things Journal, 1-1. Search in Google Scholar

[15] Song, Z., Lee, I., & Zhou, Z. (2017). Intelligent power management system of apartment based on internet of things. Boletin Tecnico/Technical Bulletin, 55(20), 631-636. Search in Google Scholar

[16] Matsui, K. (2017). An information provision system as a function of hems to promote energy conservation and maintain indoor comfort. Energy Procedia, 105, 3213-3218. Search in Google Scholar

[17] Raziq, Yaqub, Sadiq, Ahmad, Ayaz, & Ahmad, et al. (2016). Smart energy-consumption management system considering consumers’ spending goals (sems-ccsg). International Transactions on Electrical Energy Systems.1570-1584. Search in Google Scholar

[18] Mhdawi, A., & Al-Raweshidy, H. S. (2018). Iprdr: intelligent power reduction decision routing protocol for big traffic flood in hybrid-sdn architecture. IEEE Access, 10944-10955. Search in Google Scholar

[19] Chi, Zhang, Bin, Su, & Kaile, et al. (2019). Analysis of electricity consumption in china (1990–2016) using index decomposition and decoupling approach. Journal of Cleaner Production.,209:224-235. Search in Google Scholar

[20] Fma, A., Pag, A., Hkj, A., & Lk, B. (2021). Residential electricity consumption and household characteristics: an econometric analysis of danish smart-meter data. Energy Economics. 05341-. Search in Google Scholar