Method for predicting electric quantity of large-scale distribution network based on regional load analysis

Abstract

The invention provides a method for predicting electric quantity of a large-scale distribution network based on regional load analysis, and belongs to the field of power grid planning. The predicting method comprises the following steps of: dividing power supply regions; selecting a power supply region; calculating correction coefficient; performing electric quantity prediction on the selected power supply region; and predicting the electric quantity of the distribution network according to the electric quantity prediction result of the selected region. The method is high in precision, quick in calculation, strong in actual operability, simple in principle and good in flexibility; the method greatly reduces the workload of electric quantity prediction data acquisition and analytical calculation of the large-scale distribution network, and can be applied to electric quantity prediction of large-scale distribution networks of all regions in China; and by dividing the power supply regions and analyzing the load of each power supply region, the economic development patterns and the load development characteristics of the power supply regions can be better grasped, and important basis is provided for formulating more focused power grid construction schemes.

Description

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AI Technical Summary

Benefits of technology

The technical effect described by this patented technology allows us to accurately calculate high speed and powerful calculations while being easy to use at different locations throughout our system's life cycle. It also simplifies the process of collecting and analyzing data from multiple sources across vast areas or over time. This helps improve energy efficiency and planning decisions based on factors like demand levels and resource allocation.

Problems solved by technology

This patented describes three technical problem areas: (1) how accurately predicts future electrical demand based upon current operating conditions such as voltage levels at specific locations within each region (regional) during peak hours, (2) selecting optimal methods from these available options depending on their own performance criteria, (3) combining multiple types of estimators like traditional ones, including both partial load analysis techniques and load estimation algorithms, resulting in more accurate estimates than existing methods while reducing complexity and time needed.

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