With the rapid development of wind energy, probabilistic forecasting of wind power becomes increasingly crucial for reliable operations of power grids. This paper proposes a wind power interval prediction method based on temporal data soft clustering and similarity measurement (SCSM). First, a soft clustering module is used to cluster wind power data with probabilities. Next, a similarity measurement module assesses the similarity between wind power data based on soft clustering results and generates probability interval predictions by referring to historical prediction errors. Finally, the effectiveness of the proposed method is validated using real wind power data.
KEYWORDS: Electromechanical design, Patents, Analytical research, Mining, Chemical elements, Data modeling, Product engineering, Data storage, Data acquisition, Computer aided design
The core of requirement analysis is to fully and accurately obtain customer requirements from requirement data. Nowadays, with the development of computer technology and e-commerce, a large number of personalized electromechanical products requirement data appear on various network platforms. The traditional requirement analysis methods are difficult to deal with it. This paper proposed a method of requirement identification and disassembly of electromechanical products based on domain knowledge network. The existing electromechanical domain knowledge is reused to identify and disassemble the requirement text expressed by the customer in the unstructured form of natural language. Based on this method, this paper developed a computer-aided requirement analysis tool, which can assist designers to quickly clarify the requirement.
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