Data Transaction Pricing Model for Energy Big Data Centers

This paper studies and proposes an energy data transaction method based on the combination of multi-layer hierarchical fusion fuzzy feature mapping method (MLHFFFM) and long short-term memory neural network (LSTM). First, analyze the big data collected to determine the type of missing data. On this basis, MLHFFFM and fuzzy C-means algorithm (FCM) are used to cluster electricity big data, and the LSTM neural network is trained based on the characteristic values of different users and historical data, and different pricing strategies are designed for different data types. Finally, the feasibility and reliability of the improved method proposed in this paper are verified through the data analysis and comparison experiment of the public data set.