Big data analysis and prediction method of multidimensional space-time features of energy internet based on fuzzy rough model

Abstract

Nowadays, energy has become a hot issue of concern to the whole society. With the unbalanced distribution of resources in the world and more severe climate change, the constraints of resource conditions and environmental status on global energy development are becoming stronger and stronger. The rapid development of the Internet, as well as the proposal of the energy Internet, has a better application in the analysis of energy demand, which can effectively alleviate the contradiction between energy and environment. Aiming at the big data of energy Internet and based on the advantages of fuzzy rough model, this paper studies a method of big data analysis and prediction of multidimensional space-time characteristics of energy Internet based on fuzzy rough model. Firstly, according to the spatio-temporal characteristics of energy Internet data, extract the multidimensional spatio-temporal characteristics of energy internet. Secondly, rough set and fuzzy set are two commonly used mathematical tools, and the combination of the two fuzzy rough models can more fully mine data information. In view of the shortcomings of the commonly used fuzzy rough set reduction algorithm, a reduction algorithm based on conditional entropy is proposed. Finally, taking multidimensional space-time characteristics as input, combining the advantages of fuzzy rough model and neural network, a prediction model is established to analyze and forecast energy demand. The simulation experiments show that the design method is feasible and superior, and can achieve the prediction of energy demand well, so as to make more rational use of energy.