KEYWORDS: Fusion energy, Data fusion, Deep learning, Data modeling, Education and training, Data processing, Data storage, Online learning, Sensors, Data mining
With the rapid growth of energy big data and types of sensors for energy data monitoring, a series of problems have been encountered in power data quality and data fusion. To solve these problems, we proposed a novel multi-source power data fusion method based on deep learning, in which the training network of energy big data is established. By adopting the idea of incremental learning and offline learning, the MCS-RF framework of energy big data is built in the online training of real-time image data, which can effectively mitigate the sparse problem of big data and encode the discrete data into one tailored for association rules. In this way, the redundant information in energy big data would be eliminated. We compared this new method with the traditional residual-based algorithms based on the power flow data in the SCADA system, and experimental results show that the proposed method can reduce the requirements for identifying energy data with bad noise and achieve higher accuracy.
KEYWORDS: Data transmission, Data integration, Power grids, Data centers, Blockchain, System integration, Data processing, Computer security, Data storage, Information fusion
The current traditional electric power big data integration management method realizes the intelligent scheduling and management of electric power data through load prediction of electric power data, which leads to poor data transmission performance due to the lack of pre-processing of the original data. In this regard, a research on the integrated management method of electric power big data based on the grid data center is proposed. By using asymmetric encryption technology to generate private, cipher and public keys of blocks, the signature encryption and login authentication of the collection layer are completed, pre-processing of power data is realized, and power management security analysis is conducted, and the integrated power big data management architecture is proposed. In the experiment, the data transmission performance of the proposed electric power data integration management method is verified. The analysis of the experimental results shows that the power data integration management platform built by the proposed method has a low transmission delay and has a better data transmission performance.
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