Short-term load prediction method of variational mode decomposition and deep belief network

Abstract

The invention discloses a short-term load prediction method of a variational mode decomposition and deep belief network. The method comprises the following steps of 1) using a variational mode decomposition method to decompose original historical load data into a series of mode functions with different characteristics; 2) using an approximate entropy to calculate each modal function complexity, merging the modal functions whose approximate entropy values are similar into a new component, and carrying out characteristic analysis on each component; 3) in order to calculating correlation of an influence factor and an output variable, carrying out normalization processing on data; 4) combining a period characteristic of a load, and using a mutual information theory to select an input variable set from aspects of a historical load, a meteorology factor, a date type and the like; and 5) constructing the short-term load prediction method based on the deep belief network (DBN), and verifying method validity through a load prediction scene before 24h. By using the method, short-term load prediction precision is effectively increased and an electric power system load prediction problem can be well solved.

Description

technical field [0001] The invention relates to a short-term load prediction method of a power system, which is used to predict the load of the power system and belongs to the technical field of power systems. Background technique [0002] The short-term load forecasting of the power system is based on the historical load change law, combined with meteorological, economic and other factors to scientifically predict the load in the next few days or hours. Accurate load forecasting is an important decision-making basis for arranging power production scheduling and equipment maintenance planning. Therefore, it is necessary to study new methods and new technologies of load forecasting to improve the accuracy and reliability of load forecasting and meet the requirements of engineering technology. [0003] Today, with the construction and development of smart grids and the installation and application of intelligent sensing equipment such as advanced measurement systems, power sy...

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Problems solved by technology

[0005] Purpose of the invention: The present invention aims at the problems existing in the existing power system load forecasting technology, such as when faced with a large amount of refined load data, the general load forecasting method has low operating efficiency, and it is difficult to deal with high-dimensional, complex and nonlinear regression problems, and provides A fast and efficient short-term load forecasting method based on deep belief network for dealing with massive load samples

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