An accurate short-term load forecasting plays an important role in modern power system’s operation and economic development. However, short-term load forecasting is affected by multiple factors, and due to the complexity of the relationships between factors, the graph structure in this task is unknown. On the other hand, existing methods do not fully aggregating data information through the inherent relationships between various factors. In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC. GLFN-TC uses the graph learning module to automatically learn the relationships between variables to solve problem with unknown graph structure. GLFN-TC effectively handles temporal and spatial dependencies through two modules. In temporal convolution module, GLFN-TC uses dilated 1D-CNN to extract temporal dependencies from historical data of each node. In densely connected residual convolution module, in order to ensure that data information is not lost, GLFN-TC uses the graph convolution of densely connected residual to make full use of the data information of each graph convolution layer. Finally, the predicted values are obtained through the load forecasting module. We conducted five studies to verify the outperformance of GLFN-TC. In short-term load forecasting, using MSE as an example, the experimental results of GLFN-TC decreased by 0.0396, 0.0137, 0.0358, 0.0213 and 0.0337 compared to the optimal baseline method on ISO-NE, AT, AP, SH and NCENT datasets, respectively. Results show that GLFN-TC can achieve higher prediction accuracy than the existing common methods.

Web-service composition provides more powerful service functions through integrating existing Web services on the Internet, and collaborative working and resource-sharing among enterprises on the Internet have become a popular trend by means of Web-service composition technologies. Network manufacturing plays an important role in the manufacturing domain and can be realized based on Web-service composition. However, heterogeneity exists widely in complicated Web-service composition, except for manufacturing resources, and this must be resolved. In this paper, we focus on the heterogeneity existing in the execution of composite Web services in network manufacturing, in which the heterogeneity is classified into the heterogeneity between semantic equivalent Web services and the heterogeneity between subsequent Web services in a composite service. We summarize five types of conflicts, namely Semantic Conflict, Parameter Data Type Conflict, Parameter Structure Conflict, Parameter Number Conflict, and Parameter Data Unit Conflict. According to these five types of conflicts, an ontology-based approach for resolving them is proposed, including the definition of domain business process ontology knowledge, domain and industry ontology knowledge, and meta-ontology knowledge about transformation rules. Finally, a case is given, and the implementation technologies are introduced. The ontology based on heterogeneity resolution policy should be established as middleware and applied to any domain to make Web-service composition more available.