Power load prediction method based on sparse representation

Abstract

The present invention relates to a power load prediction method based on sparse representation. The method comprises the following steps of: obtaining real power load data through an electric power company, and performing preprocessing of the data to obtain preprocessed power load data, wherein the preprocessing comprises concrete operation such as data missing, data exception and data normalization; splicing a basic dictionary, a unit matrix dictionary and an external factor dictionary to form an over-complete dictionary, wherein the over-complete dictionary comprises a training dictionary configured to solve sparse coefficient vectors and a test dictionary configured to predict future power load; employing an orthogonal matching pursuit (OMP) algorithm to solve the sparse coefficient vectors according to the preprocessing power load data and the training dictionary; and combining the test dictionary and the sparse coefficient vectors to predict the future power load. The method can effectively predict the future power load on the basis of the real power load data set and can observably improve the precision of the load prediction in a condition of adding external factors.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a power load forecasting method based on sparse representation. Background technique [0002] With the development of national economy and technology and the improvement of people's living standards, electric energy has become an indispensable secondary energy source in people's daily production and life, bringing endless convenience to people's production and life. Power load forecasting is an important research topic and one of the important development directions of power grid operation. At present, based on short-term load forecasting research theory and methods, a lot of forecasting research has been done, and many methods have been proposed, which can be roughly divided into two categories: one is the traditional method represented by time series method, such as time series method, etc. These methods The algorithm is simple, fast, and widely used, but becaus...

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Problems solved by technology

At present, based on short-term load forecasting research theory and methods, a lot of forecasting research has been done, and many methods have been proposed, which can be roughly divided into two categories: one is the traditional method represented by time series method, such as time series method, etc. These methods The algorithm is simple, fast, and widely used, but because it is essentially a linear model method, there are many shortcomings and limitations, and it cannot truly reflect the nonlinear characteristics of different load models of power companies; the other is based on artificial neural networks. The new artificial intelligence method represented by the network, the neural network has the ability of parallel distribution of information and self-learnin

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