Short-term power load predicting method based on grey theory

Abstract

The invention discloses a short-term power load predicting method based on a grey theory. The method includes the following steps: recognizing defective data in load data, and performing completion and correction on defective and mutant load data; according to the completed and corrected load data, verifying modeling feasibility of a GM (1,1) model, and utilizing a logarithm processing method to correct unqualified sequences; correcting (img file=' 2013106975443100004dest_path_image002.TIF' wi='26' he=' 23' / ) parameters in the GM (1,1) model built in step 2; forming different predicting schemes by selecting data sequences from different prospectives and utilizing the GM (1,1) model after (img file=' 297985dest_path_image002. TIF' wi=' 26' he=' 23' / ) parameters corrected for predicting, sectioning a predicating day, calculating an average value of correlation coefficients of the schemes in each time section, and selecting the scheme with a biggest correlation coefficient as a predicating scheme of the time section; testing accuracy of the GM (1,1) model by utilizing a posterior difference checking method. By the short-term power load predicting method, the objectives of universality in predicting use and high accuracy are achieved.

Description

technical field [0001] The invention relates to data processing in the field of electric power, in particular to a short-term power load forecasting method based on gray theory. Background technique [0002] Under the premise that it is difficult to correctly select the influencing factors, it is very difficult to use the correct mathematical model to describe the power load forecasting. Algorithms based on various mathematical models have been studied extensively in China, but without combining the analysis of influencing factors and correct mathematical models, the correct load forecasting results cannot be obtained by improving the algorithm alone. The outstanding feature of short-term load forecasting is the similarity of daily changes, and it is obviously affected by weather factors and special events. And the load of the power system is uncontrollable in nature, so the most effective way to predict future load changes is to observe the historical record data of load, ...

AI Technical Summary

Problems solved by technology

The disadvantage is that there is no feasibility test for the GM(1,1) model, but the GM(1,1) model is blindly applied

However, the existing forecasting models have the defects that they cannot adapt to the forecasting needs of different regions, and the forecasting results are not complete and accurate.

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