User electricity consumption relevant factor identification and electricity consumption quantity prediction method under environment of big data

Abstract

The invention provides a user electricity consumption relevant factor identification and electricity consumption quantity prediction method under the environment of big data. Multiple electricity consumption modes of users are mined and existing electricity consumption behavior analysis methods are expanded by applying a mass user electricity consumption characteristic subspace clustering analysis method based on the research of the user electricity consumption characteristic evaluation index by aiming at the characteristics that the big data relevant to electricity consumption quantity prediction are various, large in size, high in dimension and high in generation speed. Meanwhile, group division is performed on the users according to different electricity consumption modes, factors relevant to user group electricity consumption quantity are identified from the aspects of regional and industry economic data, weather conditions and electricity price by utilizing mutual information matrixes, and an electricity consumption quantity big data prediction model based on a random forest algorithm is constructed so that data driving of the whole process of electricity consumption prediction is realized, adverse influence on electricity consumption quantity prediction caused by difference of the electricity consumption modes can be avoided, and thus the method has relatively high prediction precision and is suitable for big data analysis and processing.

Description

technical field [0001] The invention relates to a method for identifying factors related to user electricity consumption and predicting electricity consumption in a big data environment. Background technique [0002] Accurate electricity consumption forecast has important guiding significance for power grid planning and management decision-making of economic departments. Carrying out electricity consumption forecasting on the basis of studying the electricity consumption characteristics of different users can help power companies better understand users' personalized service needs, and provide data support for future power grid development and power demand-side response policy formulation. With the continuous development of my country's social economy and the continuous adjustment of industrial structure, the characteristics of power consumption of power users are showing a trend of diversification. The diversification of power consumption characteristics of users poses chal...

AI Technical Summary

Problems solved by technology

However, these methods either rely too much on the quality of available data, or require human intervention when identifying the factors affecting user electricity consumption, and cannot dig out the deep correlations hidden behind changes in electricity demand. Quantity forecasting has great limitations

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