-Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

Software-Defined Networking (SDN) has been one of the most promising candidates for future Internet architectures, and it may be one of the best candidates for the future communication system of Smart Grid. Moreover, SDN facilitates a variety of technology innovations in network security area. In this paper, a high-speed malicious traffic generating method called as MTG is proposed, to test the security of future network architectures. MTG can assemble packets and send them at a very high speed up to 10Gbps using hardware-based methods, and shape the sent traffic to follow different patterns with the help of a dynamically interval-between-packets adjusting mechanism. Moreover, the performance of MTG is evaluated. To the best of our knowledge, this is the first attempt to design a hardware-based high-speed malicious traffic generating method for SDN.

In order to deal with the massive, heterogeneous and distributed power data, it is urgent to use new information technology to process and manage power data in smart grid. In this paper, an efficient and secure data aggregation scheme in smart grid is proposed (ESDAS). This paper first models the distributed and hierarchical data aggregation architecture of smart grid by utilizing mathematical method. In order to find the optimal aggregator placement, a distributed cost update algorithm is also proposed. By introducing the expired timer parameter considering link states, the proposed algorithm can significantly reduce the communication cost of data aggregation. Finally, for the purpose of dealing with internal false data injection attacks, the paper presents an effective solution by establishing rule matching set of users' data. We show with simulation that ESDAS can improve the efficiency and security of smart grid.