A framework for short-term activity-aware load forecasting
Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities
Towards Fault-Tolerant Fine-Grained Data Access Control for Smart Grid
Wireless Personal Communications: An International Journal
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Smart grid presents the largest growth potential in the machine-to-machine market today. Spurred by the recent advances in M2M technologies, the smart meters/sensors used in smart grid are expected not to require human intervention in characterizing power requirements and energy distribution. These numerous sensors are able to report back information such as power consumption and other monitoring signals. However, SG, as it comprises an energy control and distribution system, requires fast response to malicious events such as distributed denial of service attacks against smart meters. In this article, we model the malicious and/or abnormal events, which may compromise the security and privacy of smart grid users, as a Gaussian process. Based on this model, a novel early warning system is proposed for anticipating malicious events in the SG network. With the warning system, the SG control center can forecast such malicious events, thereby enabling SG to react beforehand and mitigate the possible impact of malicious activity. We verify the effectiveness of the proposed early warning system through computer-based simulations.