KEYWORDS: Data fusion, Fusion energy, Geographic information systems, Machine learning, Data storage, Classification systems, Power grids, Feature extraction, Network architectures, Information fusion
Due to the diversity of data types and data organization methods of electric multi-source heterogeneous data, the organization and storage requirements of heterogeneous data are also different, so the difference of heterogeneous data must be considered for data integration and fusion, the integration and fusion level of power distribution and distributed new energy data resources needs to be improved. The distribution network data resources including electrical equipment, spatial information, grid topology, power consumption information, operating conditions and other types of resources, taking into account the new energy, are obviously different in terms of quantity, scale, data model, data type, organization mode and other aspects. The data of distribution network and distributed energy comes from business systems in multiple professional fields, and there is a strong correlation between data resources at the business level. However, due to the certain independence between the data of various business systems for power distribution and the differences in field definitions and descriptions, traditional key field matching methods are difficult to achieve automatic data matching, and data fusion across business systems is faced with the problem of no uniform rules to follow.
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