Power system short-term load prediction method, system and device and medium

Abstract

The invention belongs to the field of electric power system dispatching control, and discloses an electric power system short-term load prediction method, system and device, and a medium, and the method comprises the steps: obtaining load data and load influence data of a preset time period before a to-be-predicted time period of an electric power system; obtaining load influence data of a to-be-predicted time period of the power system; and according to the load influence data of the to-be-predicted time period of the power system and the load data and the load influence data of the preset time period before the to-be-predicted time period of the power system, performing prediction through a preset load prediction network model to obtain a load prediction result of the to-be-predicted time period of the power system. Based on the time convolution network model and the load prediction network model, data processing can be executed in parallel, and the load prediction speed of the powersystem is greatly improved; meanwhile, the load prediction network model based on the time convolution network model is adopted for prediction, compared with an existing LSTM model, more historical information can be reserved, and then the high accuracy requirement of short-term load prediction of the power system is guaranteed.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to View More

AI Technical Summary

Benefits of technology

This patented technology allows for faster and accurate analysis of electricity systems during peak hours compared to previous methods that took months or years ago. It uses advanced techniques such as machine learning algorithms trained over past performance records from real world sources like logs collected through sensors attached to electrical equipment (EEG) devices. By predicting future loads based upon these models, this approach provides an efficient way to manage energy resources effectively while meeting their needs quickly without having excessive costs associated with traditional approaches.

Problems solved by technology

This patents describes different ways that can help estimate how much energy needs during peak hours or when there'll come back down from low levels due to economic concerns like shutdown operations. One way involves measuring electrical activity over longer periods called chronograms, where certain variables may indicate future use patterns with high probability. Another approach suggests utilizing machine learning techniques instead of traditional approaches involving complex mathematical equations.

Images