AC-DC power distribution network load prediction method based on ensemble learning

Abstract

The invention discloses an AC / DC power distribution network load prediction method based on ensemble learning, and the method comprises the steps of carrying out the load data filling and normalization on the original load data, sampling a load sample input vector and a sample label through a sliding time window, and constructing a training data set; establishing a gradient lifting model, settingthe number of weak learners, and establishing a plurality of shallow neural networks to fit the negative gradient of the gradient lifting algorithm to obtain a combined prediction model; and selectinga load vector before a to-be-predicted time point as an input vector by using the sliding time window, and determining a load prediction value in combination with an ensemble learning model. According to the invention, the load prediction is carried out by fusing the strong learners of multiple models, so that the precision of load prediction is improved.

Description

technical field [0001] The invention relates to the field of load forecasting of an AC-DC distribution network, in particular to an integrated learning-based load forecasting method for an AC-DC distribution network. Background technique [0002] With the grid integration of new energy in the distribution network and the rapid development of power electronic equipment, the energy distribution in the power grid has undergone great changes. Since the access of different types of loads in the AC-DC distribution network will lead to sharp changes in load power, accurate load forecasting is required to ensure the optimal dispatch of the AC-DC hybrid distribution network, which is important for the safe and stable operation of the distribution network. significance. Traditional load forecasting methods mainly revolve around a single machine learning model, such as support vector regression, neural network and other models. A single model is prone to overfitting, increasing the g...

AI Technical Summary

Problems solved by technology

A single model is prone to overfitting, increasing the generalization error of load forecasting, thereby reducing the forecasting accuracy, and a single machine learning model is sensitive to load anomalies, and is more prone to overfitting for load forecasting of AC and DC distribution networks Phenomenon

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