Energy demand condition density prediction method

Abstract

The invention relates to an energy demand condition density prediction method. The method comprises the following steps of establishing a support vector quantile regression module; establishing a support vector weighing quantile regression module for energy demand; estimating the parameters of the models; predicting the condition density, and the like. The method has the beneficial effects that by combining the advantages of non-linear processing capability of a support vector machine and complete description capability of quantile regression on the condition distribution feature, the support vector quantile regression module for predicting the energy demand is established; on one hand, the non-linear structure of an energy system in a low-dimension space is mapped into a high-dimension space by the support vector machine, and is converted into a linear structure, so the complexity of modeling is reduced; on the other hand, the change rule of the whole condition distribution of energy demand is depicted by the quantile regression, and more available information is provided; a non-parameter kernel density estimation technology is adopted to establish the energy demand condition density prediction method, and the complete prediction of whole condition distribution feature of energy demand is realized.

Description

technical field [0001] The invention belongs to the technical field of forecasting theory and methods, in particular to a method for forecasting energy demand condition density. Background technique [0002] Energy plays a fundamental role in the operation and development of the national economy. Undoubtedly, accurate analysis and prediction of energy consumption demand is conducive to the formulation of reasonable energy management policies to ensure national energy security. However, the energy demand system is a complex system that exhibits highly nonlinear characteristics. How to truly reveal the nonlinear characteristics in the energy demand system and accurately forecast energy consumption demand has always been the focus of management departments. [0003] Energy demand forecasting has its own complexities, and its forecasting methods can be divided into four categories. The first category is statistical theory and methods, mainly including: regression analysis, in...

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Problems solved by technology

At the same time, if the parameter method is used to describe the nonlinear structure in the energy demand system, there may be problems such as model missetting, resulting in inaccurate prediction results

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