Industrial user electric quantity prediction method based on mode extraction and error adjustment

Abstract

The invention relates to the technical field of industry user load prediction, in particular to an industry user electric quantity prediction method based on mode extraction and error adjustment, and the method comprises the following operation steps: S1, employing a K-means clustering algorithm to extract a typical industry annual load curve; s2, analyzing the correlation degree between external macroscopic factors such as holidays and festivals, weather and the like and the electric quantity of the user to be predicted by using a maximum information coefficient method; s3, according to a typical industry load curve and external factors such as holidays and festivals, weather and the like, electric quantity prediction is carried out by utilizing a sparrow to search the optimized BP neural network; s4, a non-parameter estimation method is utilized, probability distribution of residual errors is obtained, a prediction result is adjusted, and a final electric quantity predicted value is obtained.According to the method, the maximum information coefficient method is adopted, the influence degree of external factors on the electric quantity is analyzed, universality and fairness are achieved, and the influence degree of different factors on the electric quantity can be described more accurately.

Description

technical field [0001] The invention relates to the technical field of industrial user load forecasting, in particular to a method for forecasting industrial user power consumption based on pattern extraction and error adjustment. Background technique [0002] Power load forecasting is the basis for the implementation of various customer-oriented applications for power consumption, and has always attracted the attention of power researchers. With the help of a large amount of data, we can more accurately predict the power consumption patterns and power consumption of users in different industries. On the one hand, it can help us better understand the electricity consumption behavior of different industries and provide better electricity service; on the other hand, it can also help the grid company to further improve the electricity sales plan and adjust the working mode of the grid, which can not only save energy and cost , It can also improve efficiency. With the rapid econ...

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Problems solved by technology

[0003] In recent years, my country's industrial structure has undergone tremendous changes, from agriculture-based to industry-based, and the tertiary industry has developed rapidly. With the changes in industrial structure and the development of emerging industries, the characteristics of electricity consumption in different periods are bound to change. Therefore, The traditional forecasting model that regards all electricity-consuming industries as a whole has flaws, and forecasting by ignoring the inherent characteristics of the electricity-consuming industries will result in forecasting errors

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