Customer Value of Smart Grid Application

The authors investigate customer value of smart grid application in smart city from the perspective of main research paradigms of customer value. Data is based on questionnaire for customers (N=131), deep interviews among specialists (7=N), and two months of observation. The results show that the typical user of smart grid technology is a male aged between 30 and 69 who considers that using the application is interesting because of the perceived benefits. Developing strong customer relationship is formed through the provision of e-service quality that has a key role in maintaining customer trust, satisfaction, and loyalty. End-product and service process paradigms to measure customer value do not fully take the complex context of smart cities into consideration, and the ecosystem paradigm must be developed to analyze customer value in smart cities in interactive dynamic decentralized environment where cumulative big data is used to match the customer needs with new digital services.

Intelligent Classification Method of Low Occupancy Big Data Based on Grid Index

In order to enhance the load balance in the big data storage process and improve the storage efficiency, an intelligent classification method of low occupancy big data based on grid index is studied. A low occupancy big data classification platform was built, the infrastructure layer was designed using grid technology, grid basic services were provided through grid system management nodes and grid public service nodes, and grid application services were provided using local resource servers and enterprise grid application services. Based on each server node in the infrastructure layer, the basic management layer provides load forecasting, image backup, and other functional services. The application interface layer includes the interfaces required for the connection between the platform and each server node, and the advanced access layer provides the human-computer interaction interface for the operation of the platform. Finally, based on the obtained main structure, the depth confidence network is constructed by stacking several RBM layers, the new samples are expanded by adding adjacent values to obtain the mean value, and the depth confidence network is used to classify them. The experimental results show that the load of different virtual machines in the low occupancy big data storage process is less than 40%, and the load of each virtual machine is basically the same, indicating that this method can enhance the load balance in the data storage process and improve the storage efficiency.

Full-scale self-propulsion simulation with a discretized propeller

A comparison between towing tank testing and full-scale CFD simulations is presented at three different target speeds. For the current self-propulsion simulation, the self-propulsion point was obtained using polynomial interpolation. The studies of boundary layer thickness, a basic grid uncertainty assessment and verification were performed to give some confidence of grid application to current self-propulsion simulation. All simulations are performed using a commercial CFD software STAR-CCM+. It is concluded that with high-fidelity numerical methods, it’s possible to treat hull roughness and directly calculate full-scale flow characteristics, including the effects of the free surface, none-linearity, turbulence and the interaction between propeller, hull and the flow field.