Short-term power load forecasting method

Abstract

The invention relates to a short-term power load forecasting method which is characterized by comprising the steps of: selecting a similar day set; constructing a RBF (Radial Basis Function) neural network forecasting model optimized by a bat algorithm; forecasting a power load on a forecasting day by utilizing the RBF neural network forecasting model optimized by the bat algorithm; and the like.According to the invention, on the basis of conventional gray correlation analysis, a similar day with a higher similarity is selected by adopting a distance similarity and shape proximity correlatedcomprehensive gray correlation degree, the defect that a conventional gray correlation analysis method only considers a geometric similarity degree among data sequences, but ignores a number proximitydegree when selecting the similar day is made up, and forecasting accuracy is improved; a weight of a RBF neural network is optimized by utilizing the bat algorithm, so that the defect that the RBF neural network is easy to fall into local optimization can be overcome, a convergence rate of the integral network is improved and computation efficiency of the integral network is improved; and the short-term power load forecasting method has the advantages of scientificity, reasonability, high applicability, good effect and the like.

Description

technical field [0001] The invention relates to the field of electric load forecasting, and relates to a short-term electric load forecasting method. Background technique [0002] Short-term power load forecasting is an important part of power grid production planning and operation scheduling. It mainly predicts the power load in the future, a day or even a few days, and is used to arrange short-term scheduling plans and respond to emergencies. Therefore, it is very necessary to adopt advanced forecasting methods. [0003] Correlation analysis is a method proposed by the gray system theory to analyze the correlation degree of various factors in the system. Its basic idea is to judge the correlation degree according to the similarity of the data sequence. It is often used in the field of power load forecasting to find similar correlation effects with the forecast date The factor's historical load days. However, the similar days found by this method often only have good load...

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Problems solved by technology

[0006] The technical problem to be solved by the present invention is: provide a short-term power load forecasting method that is scientific and reasonable, has strong applicability, and good effect, and strives to solve the problem of low power load forecasting accuracy, and the RBF neural network is easy to fall into local optimum, and the solution accuracy is low. low problem

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