Big Data Analysis of Power Market Energy Economics

Hui Liu (), Nikolaos Nikitas (), Yanfei Li () and Rui Yang ()
Additional contact information
Hui Liu: Central South University
Nikolaos Nikitas: University of Leeds
Yanfei Li: Hunan Agricultural University
Rui Yang: Central South University

Chapter Chapter 6 in Big Data in Energy Economics, 2022, pp 137-168 from Springer

Abstract: Abstract The correlation between energy consumption and national economic growth is analyzed in this chapter. At the same time, in view of the existing problems in the city electricity price, the city electricity price metering charge adjustment scheme is put forward. Finally, the feasibility of the method is verified by experiments, and the experimental results are summarized. Compared with empirical mode decomposition and wavelet packet decomposition, the Empirical Wavelet Transform (EWT) decomposition algorithm can better identify and extract the features of complex electricity price data. The Long Short-Term Memory (LSTM) network is outstanding in the application of electricity price prediction, and its performance is better than that of deep belief network and extreme learning machine. The combined model of EWT and LSTM has high prediction accuracy and good robustness. Grey correlation analysis is used to analyze the relationship between energy consumption and national economy. Through the establishment of grey correlation model, the experiment shows that, different industries and different energy types have different correlations with China's economic growth. In terms of metering and charging of urban electricity prices, cluster analysis based on K-means algorithm is carried out to divide different electricity consumption groups and optimize the residential ladder electricity price.

Keywords: Metering charge adjustment scheme; Electricity price forecasting; Grey relational analysis; K-means algorithm (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-981-16-8965-9_6

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