Short-term electric power load prediction modeling method

Abstract

The invention discloses a short-term electric power load prediction modeling method, comprising the steps that based on the feature of electric load and by means of core major constituent analysis, non-linear feature extraction is conducted on the affecting factors influencing the electric load; redundancy and co-linear information among variables can be eliminated, and the principal component feature components affecting the electric load can be obtained; the electric load can be predicted by means of BP nerve network having strong non-linear analysis capability. The invention is advantageousin that under the precondition that more information of the original data is fully reserved, the core major constituent analysis effectively conducts feature dimension reduction on the electric loaddata, and thereby the structure of the BP nerve network is optimized, and prediction speed and precision can be greatly improved.

Description

technical field [0001] The invention belongs to the field of power load forecasting, and more specifically, the invention relates to a modeling method for short-term power load forecasting. Background technique [0002] With the development of the economy and the increase in the utilization rate of electrical appliances, the power grid energy management system has become one of the factors that must be considered in regulating the electricity consumption of residents and units. Short-term power load forecasting can carry out power system planning, marketing, market transactions, Scheduling and other work, traditional forecasting methods include time series method, regression analysis method, fuzzy theory and other methods, but traditional forecasting methods are affected by many factors, redundant and collinear information lead to very complex network structure, and slow network operation speed. This limits the application of the model. Contents of the invention [0003] ...

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Problems solved by technology

[0002] With the development of the economy and the increase in the utilization rate of electrical appliances, the power grid energy management system has become one of the factors that must be considered in regulating the electricity consumption of residents and units. Short-term power load forecasting can carry out power system planning, marketing, market transactions, Scheduling and other work, traditional forecasting methods include time series method, regression analysis method, fuzzy theory and other methods, but traditional forecasting methods are affected by many factors, redundant and collinear information lead to very complex network structure, and slow network operation speed. This limits the application of the model

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