Power load forecasting method based on long short term memory neuron network

Abstract

The invention discloses a power load forecasting method based on a long short term memory (LSTM) neuron network. The power load forecasting method comprises inputting power load data and a region feature factor at a historic moment through an input unit; carrying out training and modeling on the power load data and the region feature factor at the historic moment by means of an LSTM network in order to generate a deep neural network load forecasting model which is a single-layer multi-task deep neural network model or a double-layer multi-task deep neural network model used for power supply load forecasting; forecasting the power load in an area needing to be forecasted by means of the deep neural network load forecasting model, and generating a forecasting result of the power load in the area; and outputting the forecasting result of the power load in the area through an output unit. According to the invention, a multi-task learning power load forecasting model is constructed based on the LSTM network in the deep learning field, power consumption loads in multiple areas can be forecasted accurately, and the forecasting effect is improved.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a power load forecasting method based on a long-short-term memory neural network. Background technique [0002] The power load forecasting problem aims to predict the power demand of a single or multiple transmission lines in the power grid. According to the forecast time span, it can be divided into: short-term forecasting (a few minutes to a week), medium-term forecasting (a month to a quarter) and long-term forecasting. Forecast (over one year). Under the current technical conditions, it is difficult to effectively store electric energy in large-scale power storage devices. Therefore, under the condition of meeting the power supply demand, reducing the remaining power generation as much as possible is an effective way to reduce costs and improve the efficiency of electric energy use. Therefore, using various forecasting methods to accurately predict the medium ...

AI Technical Summary

Problems solved by technology

The existing neural network-based forecasting methods can rarely predict the cross-regional po

Images