Power distribution network line load prediction method based on deep learning

Abstract

The invention relates to a power distribution network line load prediction method based on deep learning. The method comprises the following steps: obtaining historical load data and meteorological data of a to-be-predicted line; combining the load data and the meteorological data of each time period to obtain a plurality of original data; preprocessing the original data through a preprocessing module to obtain a sample data set; establishing a power distribution network line load prediction model, the power distribution network line load prediction model comprises a preliminary prediction module and an optimization prediction module, the preliminary prediction module is trained through the sample data set, the sample data set is predicted through the trained preliminary prediction module, and a preliminary prediction result set composed of preliminary prediction results is obtained; training an optimization test module through the preliminary prediction result set to obtain a trained optimization prediction module; and predicting the power distribution network line load. According to the method, the prediction precision of the to-be-predicted line load can be effectively improved by utilizing different dimension characteristics of the data related to the power grid line load.

Description

technical field [0001] The invention relates to a deep learning-based load forecasting method for distribution network lines, which belongs to the technical field of power grid load forecasting. Background technique [0002] Accurately predicting the load can formulate cost-effective power generation plans, rationally arrange unit output, reduce unnecessary waste of resources, and at the same time provide users with safe and reliable electric energy continuously, ensure the safe and stable operation of the power system, and reduce power generation costs ,Improve economic efficiency. [0003] At present, the most commonly used load forecasting methods are: time series method, regression analysis method, gray forecasting method, machine learning method, etc. These methods mainly use a single model for load forecasting, the forecasting accuracy is not high enough, and the forecasting effect is not very ideal. The current power system system is becoming more and more complex, ...

AI Technical Summary

Problems solved by technology

These methods mainly use a single model for load forecasting, the forecasting accuracy is not high enough, and the forecasting effect is not very ideal

The current power system system is becoming more and more complex, and various conventional load forecasting methods have been difficult to meet the increasingly high load forecasting accuracy requirements of the power sector.

Images