Energy prediction method for optimizing gray model key parameters based on empire butterfly algorithm

Abstract

The invention relates to an energy prediction method for optimizing gray model key parameters based on a king butterfly algorithm, and the method is technically characterized in that a gray GM (1, 1) prediction model is established for initial energy demand data; aiming at a development coefficient a and a grey action quantity u of the grey GM (1, 1) model, establishing an objective function of an average relative error between an initial energy demand value and a simulation value output by the grey prediction model; solving an optimal solution of the target function through a king butterfly algorithm, and determining a development coefficient a and a grey action quantity u of a grey GM (1, 1) model; and substituting the development coefficient a and the grey action quantity u into a grey GM (1, 1) model to predict the energy demand. The method is reasonable in design, the empire butterfly algorithm is applied to the gray GM (1, 1) prediction model, the applicability of the single gray GM (1, 1) prediction model to irregular fluctuation data caused by uncontrollable accidental factors is improved, the prediction precision of the gray algorithm is also improved, the method is relatively simple, and the prediction effect is better.

Description

technical field [0001] The invention belongs to the technical field of energy forecasting, in particular to an energy forecasting method for optimizing key parameters of a gray model based on a monarch butterfly algorithm. Background technique [0002] In order to speed up the implementation of the carbon emission peaking action, it is necessary to formulate and implement the carbon emission peaking action plan as soon as possible, promote key industries such as iron and steel to reach the peak first and coal consumption to reach the peak as soon as possible, improve the dual control system for energy consumption, and jointly promote pollution reduction and carbon reduction. Dual control of industrial pollution discharge to promote industrial green transformation. [0003] The gray theory was first proposed by Deng Julong in 1989 and has a history of more than 20 years. The theory does not rely on statistical methods to consider gray quantities, but it uses raw data indirec...

AI Technical Summary

Problems solved by technology

However, the time series of the actual system will have wave-like changes, and the traditional gray model has low prediction accuracy when dealing with this kind of data

Therefore, many models have been proposed to improve this accuracy, such as taguchigrey, gray-fuzzy, triangle-gray and other models. Although these hybrid models can improve the prediction accuracy of the gray GM (1, 1) model to a certain extent, but due to the inclusion of Complicated mathematical calculations are difficult for engineering applications

Images