Load aggregate grouping forecasting method based on gated loop unit network

Abstract

The invention relates to a load aggregate grouping forecasting method based on a gated circulation unit network, comprising the following steps: clustering user load data by using an adaptive distributed spectrum clustering algorithm, thereby obtaining a plurality of power consumption groups with similar load characteristics and obtaining load characteristic matrices of each group; Three GRU networks are constructed, and three GRU networks are trained by extracting the temporal characteristics of the population to obtain three GRU network forecasting models, and three GRU networks are fused bystochastic forest algorithm to obtain the load forecasting models of each population. The characteristics of the time to be forecasted are inputted into the load forecasting model, the load forecasting values of each group are obtained respectively, and the forecasted values of different groups are summed up to obtain the forecasted values of the final load aggregates. By introducing grouping prediction, depth neural network and model fusion method, the invention can grasp user load characteristic and variation rule, and has high prediction precision and strong applicability.

Description

technical field [0001] The invention belongs to the technical field of electric power system load forecasting, and in particular relates to a grouping forecasting method of load aggregates based on a gated cycle unit network. Background technique [0002] Accurate and fast load forecasting plays an important role in the safe and economical operation of the power system. Traditional load forecasting is divided into levels based on the physical structure of power system measurement, such as system level, bus level, substation level, microgrid level, etc. Usually, the load forecasting method developed for a specific level cannot be applied to other levels. In recent years, with the popularity of smart meters, power companies have been able to obtain massive amounts of fine-grained user load data. Based on smart meter data, it can get rid of the limitation of power system measurement structure, and can flexibly divide load aggregates of different scales according to demand and ...

AI Technical Summary

Problems solved by technology

However, LSTM has the disadvantage of long training time, and because the load aggregate contains multiple load characteristics, different neural network structures are applicable to different load characteristics.

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