Power load prediction method based on cloud model

Abstract

The invention discloses a power load prediction method based on a cloud model, and the method comprises the steps: determining a factor which exerts the biggest impact on a power load and an output factor of power load prediction; carrying out the description of the uncertainty of the obtained factors; building a two-dimensional multi-rule generator; and carrying out the prediction of the power load. According to the invention, the method forms a quantified sample database through the building of the cloud model, enables an original limited sample space to be extended to be extended sample data with the randomness and popularity through the cloud model, and irons out the defect that the sample data is difficult to collect. The method creates conditions for improving the prediction precision of a load index. According to the invention, the method is high in prediction precision, is suitable for a current large-scale power grid, and can process the uncertain information and fuzzy concepts.

Description

technical field [0001] The invention specifically relates to a cloud model-based power load forecasting method. 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. [0003] 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 because it is essentially a linear model method, there are ma...

AI Technical Summary

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 in rural power systems; the other is artificial The new artificial intelligence method represented by the neural network, the neural network has the ability of parallel distribution of information and self-learning and arbitrarily approximating continuous functions, and can capture various changing trends of short-term power loads

The BP network requires a large amount of historical data for training, and its ability to learn and deal with uncertainty and artificial information is poor

FUZZY forecasting is a forecasting method that has been emerging in power system load forecasting in recent years, but from the perspective of practical application, the accuracy of FUZZY method for load forecasting is often unsatisfactory

In addition, with the expansion of the grid scale, there are more and more factors involved in various aspects, and a large amount of uncertain information will inevitably appear. Therefore, the current commonly used load forecasting methods cannot adapt well to this aspect.

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