With the rapid development of the power infrastructures and the increase in the number of electric vehicles (EVs), vehicle-to-grid (V2G) technologies have attracted great interest in both academia and industry as an energy management technology in 5G smart grid. Considering the inherently high mobility and low reliability of EVs, it is a great challenge for the smart grid to provide on-demand services for EVs. Therefore, we propose a novel smart grid architecture based on network slicing and edge computing technologies for the 5G smart grid. Under this architecture, the bidirectional traffic information between smart grids and EVs is collected to improve the EV charging experience and decrease the cost of energy service providers. In addition, the accurate prediction of EV charging behavior is also a challenge for V2G systems to improve the scheduling efficiency of EVs. Thus, we propose an EV charging behavior prediction scheme based on the hybrid artificial intelligence to identify targeted EVs and predict their charging behavior in this paper. Simulation results show that the proposed prediction scheme outperforms several state-of-the-art EV charging behavior prediction methods in terms of prediction accuracy and scheduling efficiency.
This study surveys the developments in satellite attitude determination and control system, especially for microsats. This survey is not intended to be complete but is limited to the most significant developments of sensors, actuators, and algorithms in the last two decades. First, attitude determination methods including algorithms and sensors together with actuator-based control methods are introduced. Furthermore, current problems in alignment error, flexible satellites, and low redundancy of microsats attitude determination and control system are discussed. Moreover, developments of some deep-neural-networks-based methods, which have great potential in solving current problems, are summarized.
Big data and artificial intelligence technology, as the unified means and carrier for collecting, storing and computing core data of contemporary power IoT, has diverse and complex characteristics of its data sources and types. And the lack of insecure data access, abnormal response and terminal access rights control leads to the failure of the integrity and credibility of the closed-loop information defense. In particular, wireless sensor networks (WSNs) in power scenarios are more susceptible to malicious attacks. To address the above problems, this paper proposes a power IOT information defense strategy based on improved identity-based dynamic clustering authentication algorithm (IIDC). First, the terminal device sets the private key to solve the key escrow problem of terminal security authentication in the IOT model. At the same time, the improved algorithm dynamically generates pseudo-cryptographic matrix to avoid collusion attack. Finally, a hierarchical privilege management mechanism is adopted to decrypt the terminal authentication once. The proposed algorithm is more suitable for terminal security access and power consumption requirements in WSN-based power IOT scenario, as verified by experimental simulation.
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