نوع مقاله : مقاله پژوهشی
موضوعات
عنوان مقاله English
نویسندگان English
The aim of this research is to design a secure model based on cloud computing for smart meter reading as the Internet of Things. First, library studies are conducted and then a model is designed that receives data from smart meters and stores it in a cloud computing system, which encounters a volume of big data. In this model, the goal is to determine the network load in the first stage and then separate conventional data from unconventional data. This is the basis for security and prevention of theft from the network. Conventional data indicates theft from the network, while conventional data indicates no theft. First, load prediction is performed using machine learning algorithms and it has been shown that the random forest algorithm is 95% capable of predicting the network load based on 4 specified input variables. Then, the degree of conventionality or unconventionality of the predicted data was determined using the convolutional neural network algorithm, which has shown that the convolutional neural network algorithm is able to predict and classify conventional and unconventional data with the least error and distinguish them from each other. Therefore, this algorithm provides reliable results regarding theft from the energy network through smart meters.
کلیدواژهها English
Parast, F. K., Sindhav, C., Nikam, S., Yekta, H. I., Kent, K. B., & Hakak, S. (2022). Cloud computing security: A survey of service-based models. Computers & Security, 114, 102580.
Badr, M.M.; Ibrahem, M.I.; Kholidy, H.A.; Fouda, M.M.; Ismail, M. Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems. Energies 2023, 16, 2852. https:// doi.org/10.3390/en16062852
Sousa, E.L.d.; Marques, L.A.d.A.; Lima, I.d.S.F.d.; Neves, A.B.M.; Cunha, E.N.; Kreutz, M.E.; Neto, A.J.V. Development a Low-Cost Wireless Smart Meter with Power Quality Measurement for Smart Grid Applications. Sensors 2023, 23, 7210. https://doi.org/10.3390/s23167210
Abdalzaher, M.S.; Fouda, M.F.; Ibrahem, M.I. Data Privacy Preservation and Security in Smart Metering Systems. Energies 2022, 15, 7419. https://doi.org/10.3390/ en15197419
Quesada, C., Astigarraga, L., Merveille, C., & Borges, C. E. (2024). An electricity smart meter dataset of Spanish households: insights into consumption patterns. Scientific Data, 11(1), 59. https://doi.org/10.1038/s41597-023-02846-0
Hseiki, H. A., El-Hajj, A. M., Ajra, Y. O., Hija, F. A., & Haidar, A. M. (2024). A secure and resilient smart energy meter. IEEE Access, 12, 3114-3125.
Dahunsi, F. M., Eniola, S. O., Ponnle, A. A., Agbolade, O. A., Udekwe, C. N., & Melodi, A. O. (2021). A review of smart energy metering system projects. Jurnal Elektronika dan Telekomunikasi, 21(1), 70-78.
Haq, E. U., Pei, C., Zhang, R., Jianjun, H., & Ahmad, F. (2023). Electricity-theft detection for smart grid security using smart meter data: A deep-CNN based approach. Energy Reports, 9, 634-643.
Ravinder, M., & Kulkarni, V. (2023). Intrusion detection in smart meters data using machine learning algorithms: A research report. Frontiers in Energy Research, 11, 1147431.
Dhaou, I.B. Design and Implementation of an Internet-of-Things-Enabled Smart Meter and Smart Plug for Home-Energy-Management System. Electronics 2023, 12, 4041. https:// doi.org/10.3390/electronics12194041
Dewangan, F.; Abdelaziz, A.Y.; Biswal, M. Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review. Energies 2023, 16, 1404. https:// doi.org/10.3390/en16031404
Sayed, H.A.; William, A.; Said, A.M. Smart Electricity Meter Load Prediction in Dubai Using MLR, ANN, RF, and ARIMA. Electronics 2023, 12, 389. https://doi.org/ 10.3390/electronics12020389
Arulanandu, C. K., Murthy, S. V. D., & Nagraj, G. (2018). Cloud based RDF security: A secured data model for cloud computing. International Journal of Intelligent Engineering and Systems, 11(1), 83-93.
