Living energy consumption prediction method and system based on ARMA and regression analysis

Abstract

The invention discloses a living energy consumption prediction method and system based on ARMA and regression analysis. The method comprises the steps of obtaining a per capita living energy consumption item and measured value thereof; establishing a first sample corresponding to the measured value of the per capita living energy consumption, and constructing a time series; determining the order of an ARMA model according to the Bayes information criterion and constructing the ARMA model; establishing a sample set with an impact factor of a reality factor and a time sequence as a second sample; performing a regression analysis on the second sample and obtaining a combined prediction model; and using the combined prediction model to carry out combined prediction on the time sequence. The combined machine learning prediction model based on ARMA and regression analysis in the invention can better adapt to the characteristics of time series and accurately describe the actual influencing factors and has a beneficial effect with high test accuracy.

Description

technical field [0001] The invention relates to the field of energy prediction data mining, in particular to a living energy consumption prediction method and system based on ARMA and regression analysis. Background technique [0002] Energy occupies an important position in economic development and is an important factor affecting national strategies and policies. In recent years, the vigorous development of China's energy industry has provided a steady stream of driving force for China's economic growth. However, in the process of energy industry development, problems such as insufficient energy per capita, low energy utilization efficiency, and serious environmental pollution have become increasingly prominent. Adjusting and controlling the energy structure and energy consumption of the country, and predicting the per capita living energy consumption will help to formulate reasonable energy regulation measures, which is of great significance to the healthy development of ...

AI Technical Summary

Problems solved by technology

At present, the time series method is mostly used for energy forecasting. The time series method predicts future data by looking for potential laws in historical data. However, when a single time series model predicts nonlinear chaotic sequences, the prediction results often have large error

In addition, the value of the time series under actual conditions at a certain moment not only depends on its own changing law, but also is affected by factors such as population and economy, and the time series model cannot describe the characteristic information of realistic influencing factors

[0003] To sum up, in the prior art, there is still a lack of effective solutions for the problems that the prediction results have large errors and the time series model cannot describe the real influencing factors.

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