Regional power grid short-term load prediction method and system

Abstract

The invention relates to a regional power grid short-term load prediction method and system. The method comprises the steps: enabling a network terminal to collect the historical multivariate feature information data related to load prediction, crawling future weather prediction broadcast information from the Internet, and carrying out the data preprocessing on the collected feature information; carrying out fine granularity on meteorological information, and selecting a meteorological virtual similar day of a day to be measured; selecting a to-be-predicted daily load virtual similar day; and based on historical feature information data, determining a trained short-term load prediction model based on a deep learning theory so as to realize prediction of a future load. According to the invention, the accuracy of short-term load prediction can be improved.

Description

technical field [0001] The invention relates to the field of short-term load forecasting of electric power systems, in particular to a method and system for short-term load forecasting of regional power grids. Background technique [0002] With the development of the economy and the integration of large-scale distributed energy into the grid, the weather-sensitive load base of regional power grids continues to rise, and the daily load peak-to-valley difference continues to widen, and the local load changes appear to be more random and complex. How to scientifically and effectively combine the fine-grained meteorological characteristic data to deeply explore the relationship between relevant factors and local load changes in short-term load forecasting is an inevitable direction to further improve the accuracy of load forecasting and realize the refined work management of load forecasting. [0003] The research on short-term load forecasting of traditional regional power grid...

AI Technical Summary

Problems solved by technology

[0004] For the existing load-similar days, most of the current load-similar day selection methods still use coarse-grained daily characteristic weather as the feature quantity for similar day selection, which is difficult to map to local changes in load

And most of the methods select load similar days from historical days, and there is often an excessive partial similarity between historical days and similar days, which has a great impact on the effect of load forecasting

Images