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Electric load forecasting method and system based on convolutional long-short-term memory neural network

A long-short-term memory and neural network technology, applied in the field of electric load forecasting based on convolutional long-short-term memory neural network, can solve the problems of low accuracy of electric load forecasting, sensitive data changes, and easy over-fitting, etc., to achieve Reduce the risk of model overfitting, improve robustness, and improve the effect of accuracy

Active Publication Date: 2021-09-17
STATE GRID SHANDONG ENERGY SAVING SERVICE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above problems, this disclosure proposes a power load forecasting method and system based on a convolutional long-short-term memory neural network (Multi-viewConvLSTM Neural Network), which solves the problem of low power load forecasting accuracy, large errors, and sensitivity to data changes. Technical issues that are prone to overfitting

Method used

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  • Electric load forecasting method and system based on convolutional long-short-term memory neural network
  • Electric load forecasting method and system based on convolutional long-short-term memory neural network
  • Electric load forecasting method and system based on convolutional long-short-term memory neural network

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Embodiment 1

[0030] In the technical solutions disclosed in one or more embodiments, such as figure 1 As shown, the electricity load forecasting method based on the convolutional long short-term memory neural network includes the following steps:

[0031] Step 1. Obtain and preprocess the historical power consumption data of the power consumption system to be predicted;

[0032] Step 2. Transfer the preprocessed sample data to the trained multi-layer neural network including the MVCNN model and the ConvLSTM model, and output the prediction result;

[0033] Step 3. Output the power distribution control scheme according to the prediction result.

[0034] Optionally, the power distribution control scheme includes power distribution time allocation, power distribution area coordination, etc., and controls the control switches of corresponding lines according to the prediction results, so as to realize intelligent power distribution of each power distribution branch. Especially suitable for s...

Embodiment 2

[0102] This embodiment provides a power load forecasting system based on a convolutional long-short-term memory neural network, including:

[0103] Data acquisition and preprocessing module: configured to obtain historical power consumption data of the power consumption system to be predicted and perform preprocessing;

[0104] Prediction module: configured to transmit the preprocessed sample data to the trained multi-layer neural network including the MVCNN model and the ConvLSTM model, and output the prediction result;

[0105] Power distribution control output module: configured to output a power distribution control scheme according to a prediction result.

Embodiment 3

[0107] This embodiment provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps described in the method in Embodiment 1 are completed.

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Abstract

The present disclosure proposes a method and system for forecasting power consumption load based on convolutional long-short-term memory neural network, including the following steps: obtaining historical power consumption data of the power consumption system to be predicted and performing preprocessing; transmitting the preprocessed sample data to The trained multi-layer neural network including the MVCNN model and the ConvLSTM model outputs the prediction results; outputs the power distribution control scheme according to the prediction results. The multi-layer neural network disclosed in this disclosure combines the MVCNN model and the ConvLSTM model. The MVCNN model can not only extract information along the time series, but also extract fusion information between different time series feature data, thereby improving the accuracy of model recognition and reducing data. impact on the model. The ConvLSTM model can further process spatiotemporal information, encode spatial information, improve the feature extraction ability of the model, thereby improving the robustness of the model, and solve the problem of low accuracy of power load forecasting, large errors, sensitivity to data changes, and easy overfitting technical issues.

Description

technical field [0001] The present disclosure relates to the technical field related to intelligent power distribution, and in particular, relates to a method and system for predicting electric load based on a convolutional long-short-term memory neural network. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] With the acceleration of industrialization and electrification, human demand for energy has soared, especially the demand for electric energy, which is showing a rising trend year by year. In large buildings and buildings, short-term power supply and demand imbalances are common. How to effectively alleviate this situation and improve the utilization efficiency of electric energy is a pain point in the industry that needs to be solved urgently. Accurately and efficiently predicting the electricity demand of buildings is of great si...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02J3/00G06Q10/04G06Q10/06G06Q50/06G06N3/04
CPCH02J3/003G06Q10/04G06Q10/06375G06Q50/06H02J2203/20G06N3/045Y04S10/50
Inventor 张爱群辛卫东卞峰牛蔚然汪东军王瑞琪王硕朱国梁魏姗姗杨伟进刘建文李燕迟青青魏嘉萍王永彬
Owner STATE GRID SHANDONG ENERGY SAVING SERVICE
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