With the development of modern information technology (IT), a smart grid has become one of the major components of smart cities. To take full advantage of the smart grid, the capability of intelligent scheduling and planning of electricity delivery is essential. In practice, many factors have an impact on electricity consumption, which necessitates information fusion technologies for a thorough understanding. For this purpose, researchers have investigated methodologies for collecting electricity consumption related information and variant multi-factor power consumption forecasting models. In addition, conducting a comprehensive analysis and obtaining an accurate evaluation of power consumption are the premise and basis for a more robust and efficient power grid design and transformation. Therefore, it is meaningful to explore forecasting models that are able to reflect the power consumption changes and internal relations within fusional information effectively. Making electricity consumption forecasting based on the neural network has been a popular research topic in recent years, and the back-propagation neural network (BPNN) algorithm has been recognized as a mature and effective method. In this paper, BPNN is adopted to forecast the electricity consumption using Pecan Street, a community with a relatively large-scale smart grid, as a case study, and takes multiple factors into account, such as weather condition, weekend and holidays. The influences of each factor have been evaluated for a deeper insight. We hope this work will inspire more discussion and further study to guide the design of future smart grids.
Distributed sensors are the eyes and ears of a smart grid which provide information vital for monitoring and controlling the entire power generation, transmission, and distribution systems. Secure exchange of information among the sensing and decision-making entities is essential as failures may bring the entire system on its knees. With the rapid growth in the number of distributed sensors, drones have a myriad of applications. A swarm of drones could also be deployed in war zones and disaster-stricken areas where a secured intercommunication is of paramount importance for survivability and for successful mission completion. In this paper, a secure mechanism is proposed based on mobile agents to secure information exchange with minimum overhead. An Agent Administrator (AA) automatically clones and sends a secure mobile agent (SMA) to the target sensors or drones to scan and check their security status. Then, the dispatched SMAs send feedbacks to the server AA or other members. In case of sensors, the closest terminal unit to which the sensors are directly connected is designated as an AA, which is capable of checking authentication and scanning for vulnerabilities. In the case of drones, any one of them or multiple of them could be designated as the AA and the flagged feedback is broadcast to all other nodes or drones thereby providing them security status updates. A modified Nagle’s Algorithm is also proposed to support real-time video transmission. The experimental results validate the effectiveness and convenience of the proposed system.