IoT in Smart Grid: Energy Management Opportunities and Security Challenges

  • Motahareh PourbehzadiEmail author
  • Taher Niknam
  • Abdollah Kavousi-Fard
  • Yasin Yilmaz
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 574)


This study is focusing on presenting an online machine learning algorithm that benefits from sequential data of IoT devices in the smart grid. This method provides the smart grid operator with the historical data of generation units of a smart grid that is connected to the IEEE 33-bus test system. The proposed smart grid consists of two photovoltaic cells, two wind turbines, a microturbine, a fuel cell and an electric car the behaviour of which is considered similar to that of a storage unit. In the training phase, the optimized generation units’ data is used to form a regressive model of every unit’s behaviour. Afterwards, the model is used to predict the behaviour of every unit in the next 24 h. The optimized operation data is used to solve the optimal power flow (OPF) problem. The output of OPF is useful in monitoring the stability of the smart grid, calculating power losses and locating possible faults. Moreover, the proposed framework benefits from the online discrepancy test (ODIT) method, which uses the data of the machine learning method to form a baseline for anomaly detection. The advantage of this method is that it minimizes false alarms and it eliminates false data in anomaly detection. The implementation of the proposed solution methodology has proven to be effective in regards with execution-time reduction and accuracy.


Energy management IoT Machine learning Microgrid Security 


  1. 1.
    Sakhnini, J., Karimipour, H., Dehghantanha, A., Parizi, R.M., Srivastava, G.: Security aspects of Internet of Things aided smart grids: a bibliometric survey. Internet Things, 100111 (2019, in press)Google Scholar
  2. 2.
    Bekara, C.: Security issues and challenges for the IoT-based smart grid. Procedia Comput. Sci. 34, 532–537 (2014)CrossRefGoogle Scholar
  3. 3.
    Zheng, Z., Yang, Y., Niu, X., Dai, H.N., Zhou, Y.: Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans. Ind. Inform. 14(4), 1606–1615 (2017)CrossRefGoogle Scholar
  4. 4.
    Zhang, C., Kuppannagari, S.R., Kannan, R., Prasanna, V.K.: Generative adversarial network for synthetic time series data generation in smart grids. In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1–6. IEEE, October 2018Google Scholar
  5. 5.
    Bor, M., Marnerides, A., Molineux, A., Wattam, S., Roedig, U.: Adversarial machine learning in smart energy systems (2019)Google Scholar
  6. 6.
    Sakhnini, J., Karimipour, H., Dehghantanha, A.: Smart grid cyber attacks detection using supervised learning and heuristic feature selection. arXiv preprint arXiv:1907.03313 (2019)
  7. 7.
    Oozeer, M.I., Haykin, S.: Cognitive dynamic system for control and cyber-attack detection in smart grid. IEEE Access 7, 78320–78335 (2019)CrossRefGoogle Scholar
  8. 8.
    Askarzadeh, A.: Parameter estimation of fuel cell polarization curve using BMO algorithm. Int. J. Hydrog. Energy 38(35), 15405–15413 (2013)CrossRefGoogle Scholar
  9. 9.
    Mozaffari, M., Yilmaz, Y.: Online anomaly detection in multivariate settings. In: 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. IEEE, October 2019Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  1. 1.Department of Electrical EngineeringShiraz University of TechnologyShirazIran
  2. 2.Department of Electrical EngineeringUniversity of South FloridaTampaUSA

Personalised recommendations