More and more IoT (Internet of Thing) devices have been connected to our lives in recent years, making life more convenient. Many countries are also making use of Internet of Thing technology to carry out intelligent electricity network reform. One of the reform goals is balancing the supply and demand of electricity, which has become a top priority. Balancing electricity supply and demand through real-time electricity prices has become an effective way. However, using traditional machine learning models for real-time electricity price prediction requires complex feature engineering, and the results are not satisfactory. Also, the mainstream fusion methods use data-level fusion, which will put very high pressure on communication bandwidth and computer resources. In this paper, an LSTM- (long short-term memory-) based decision level fusion of multisource data is proposed and applied for real-time electricity price prediction on actual electricity price datasets. The method solves the difficulties of traditional machine learning models in dealing with complex nonlinear problems. It achieves local asynchronous processing of multisource data through decision-level fusion, reducing the requirement for bandwidth resources and providing perfect results in real-time electricity price prediction. The experimental results show that the prediction accuracy of the decision fusion prediction model based on LSTM is higher than that of the linear regression algorithm.
With the rapid development of computer technology and Internet, the traditional data mining methods and technologies in power industry will face great difficulties, and it is difficult to carry out accurate data processing and analysis. How to mine valuable data from a large number of original data has become a research difficulty. Aiming at this problem, this paper establishes the framework design of power enterprise central data platform based on big data. In order to further improve the actual performance of the scheme, the defects of existing algorithms are analyzed by IM_Apriori improves the calculation method, simplifies the calculation steps, reduces the calculation times, and provides technical support for enterprise data analysis. Through the analysis of the test results, when the data peak reaches 100 m, the execution time is reduced by 25s, which is obviously superior to the traditional scheme. The test results show that the design scheme in this paper has a high comprehensive performance, compared with the traditional central data platform framework, the performance has been greatly improved. Through the analysis, the research in this paper has achieved ideal results, and has made a contribution to the research on the framework design of the central data platform of power enterprises.
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