Abstract
In the era of the Internet of Things (IoT) supporting 5 G technology, the Smart Grid (SG) is an important part of Smart City. Specifically, load forecasting is a key ingredient of the sustainable development of the SG. Similarly, the data collected from the IoT devices (i.g., smart appliances, smart meters) is one of the key factors to improve the accuracy of the prediction results. However, two challenges are present in the process of data collection, where we use multi-source data for load forecasting. First, in the process of collecting data from different platforms, we should protect the users’ privacy information. Second, data loss is caused by some reasons (i.g., equipment power failure and communication failure), which affects the accuracy of load forecasting. Considering the above challenges, we propose a new distributed Locality-Sensitive Hashing (LSH) method for load forecasting, named \(LF_{dLSH}\). At last, a case study is put forward to illustrate the feasibility of our approach.
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References
Zhou, X., Liang, W., Ma, J., Yan, Z., Kevin, I., Wang, K.: 2D federated learning for personalized human activity recognition in cyber-physical-social systems. In: IEEE Transactions on Network Science and Engineering (2022)
Zhou, X., Liang, W., She, J., Yan, Z., Kevin, I., Wang, K.: Two-layer federated learning with heterogeneous model aggregation for 6G supported internet of vehicles. IEEE Trans. Veh. Technol. 70(6), 5308–5317 (2021)
Zhou, X., Liang, W., Luo, Z., Pan, Y.: Periodic-aware intelligent prediction model for information diffusion in social networks. IEEE Trans. Netw. Sci. Eng. 8(2), 894–904 (2021)
Har-Peled, S., Indyk, P., Motwani, R.: Approximate nearest neighbor: towards removing the curse of dimensionality. Theory of Computing, vol. 8, no. 14, pp. 321–350 (2012). http://www.theoryofcomputing.org/articles/v008a014
Shuo, Y., Weimin, W., Zhiwei, K., Hua, F., Yan, Z.: Smart grid data privacy protection algorithm. In: International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 242–246 (2017)
Zhang, L., Zhao, L., Yin, S., Chi, C.-H., Liu, R., Zhang, Y.: A lightweight authentication scheme with privacy protection for smart grid communications. Future Gener. Comput. Syst. 100, 770–778 (2019). https://www.sciencedirect.com/science/article/pii/S0167739X19310398
Chin, J.-X., Zufferey, T., Shyti, E., Hug, G.: Load forecasting of privacy-aware consumers. In: IEEE Milan PowerTech, 1–6 (2019)
Soykan, E.U., Bilgin, Z., Ersoy, M.A., Tomur, E.: Differentially private deep learning for load forecasting on smart grid. In: 2019 IEEE Globecom Workshops (GC Wkshps), pp. 1–6 (2019)
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1
Graves, A.: Long short-term memory long short-term memory. In: Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence, vol. 385, pp. 37–45. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24797-2_4
Kong, W., Shen, J., Vijayakumar, P., Cho, Y., Chang, V.: A practical group blind signature scheme for privacy protection in smart grid. J. Parallel Distrib. Comput. 136, 29–39 (2020). https://www.sciencedirect.com/science/article/pii/S0743731519301285
Singh, N.K., Mahajan, V.: End-user privacy protection scheme from cyber intrusion in smart grid advanced metering infrastructure.: Int. J. Crit. Infrastruct. Prot. 34, 100410 (2021). https://www.sciencedirect.com/science/article/pii/S1874548221000020
Wan, S., Xia, Y., Qi, L., Yang, Y.-H., Atiquzzaman, M.: Automated colorization of a grayscale image with seed points propagation. IEEE Trans. Multimedia 22(7), 1756–1768 (2020)
Xu, X., et al.: A computation offloading method over big data for IoT-enabled cloud-edge computing. Future Gener. Comput. Syst. 95, 522–533 (2019). https://www.sciencedirect.com/science/article/pii/S0167739X18319770
Qi, L., Xiang, H., Dou, W., Yang, C., Qin, Y., Zhang, X.: Privacy-preserving distributed service recommendation based on locality-sensitive hashing. In: IEEE International Conference on Web Services (ICWS), pp. 49–56 (2017)
Yan, C., Cui, X., Qi, L., Xu, X., Zhang, X.: Privacy-aware data publishing and integration for collaborative service recommendation. IEEE Access 6, 43021–43028 (2018)
Liu, J., Jin, T., Pan, K., Yang, Y., Wu, Y., Wang, X.: An improved KNN text classification algorithm based on simhash. In: 2017 IEEE 16th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC), pp. 92–95 (2017)
Jurado, S., Nebot, À., Mugica, F., Mihaylov, M.: Fuzzy inductive reasoning forecasting strategies able to cope with missing data: a smart grid application. Appl. Soft Comput. 51, 225–238 (2017). https://www.sciencedirect.com/science/article/pii/S1568494616306093
Peppanen, J., Zhang, X., Grijalva, S., Reno, M.J.: Handling bad or missing smart meter data through advanced data imputation. In: IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5 (2016)
Kim, M., Park, S., Lee, J., Joo, Y., Choi, J.K.: Learning-based adaptive imputation methodwith KNN algorithm for missing power data. Energies 10(10), 1668 (2017). https://www.mdpi.com/1996-1073/10/10/1668
Ryu, S., Kim, M., Kim, H.: Denoising autoencoder-based missing value imputation for smart meters. IEEE Access 8, 40656–40666 (2020)
Su, T., Shi, Y., Yu, J., Yue, C., Zhou, F.: Nonlinear compensation algorithm for multidimensional temporal data: a missing value imputation for the power grid applications. Knowl.-Based Syst. 215, 106743 (2021). https://www.sciencedirect.com/science/article/pii/S095070512100006X
Qi, L., Zhang, X., Dou, W., Ni, Q.: A distributed locality-sensitive hashing-based approach for cloud service recommendation from multi-source data. IEEE J. Sel. Areas Commun. 35(11), 2616–2624 (2017)
Qi, L., Wang, R., Hu, C., Li, S., He, Q., Xu, X.: Time-aware distributed service recommendation with privacy-preservation. Inf. Sci. 480, 354–364 (2019)
Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefficient. In: Noise Reduction in Speech Processing. Springer Topics in Signal Processing, pp. 1–4. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00296-0_5
Xia, Y., Wu, L., Zheng, X., Yu, T., Jin, J.: Data dissemination with trajectory privacy protection for 6G-oriented vehicular networks. IEEE Internet Things J. 9, 21469–21480 (2022)
Kou, G., Yi, K., Xiao, H., Peng, R.: Reliability of a distributed data storage system considering the external impacts. IEEE Trans. Reliab., 1–10 (2022)
Acknowledgements
This work was supported in part by the Dou Wanchun Expert Workstation of Yunnan Province No.202105AF150013.
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Miao, Y. et al. (2023). Distributed Power Load Missing Value Forecasting with Privacy Protection. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13655. Springer, Cham. https://doi.org/10.1007/978-3-031-20096-0_39
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