Power load prediction system based on long short term memory neural network

Abstract

The invention discloses a power load prediction system based on a long short term memory neural (LSTM) network. The LSTM network comprises an input layer, a LSTM network layer and an output layer. The system comprises an information receiving module which is used for transmitting the inputted power load data of historical moments and regional feature factors to the input layer; a model establishing module which is used for performing training and modeling on the power load data of the historical moments and the regional feature factors through the LSTM network layer so as to generate a deep neural network load prediction model; a power prediction module which is used for predicting the power load of the region by using the deep neural network load prediction model and generating the power load prediction result of the region through a regressor connected with the LSTM network layer; and a result output module which is used for outputting the power load prediction result of the region through the output layer. The multitask learning load prediction model is constructed based on the LSTM network so that the power load of multiple regions can be accurately predicted and the prediction effect can be enhanced.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a power load forecasting system 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 power load at the same time, and the proposed power supply load forecasting model is not accurate

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