Smart community electricity consumption real-time prediction method and device based on depth learning

A deep learning and real-time prediction technology, applied in the field of artificial intelligence, can solve the problems of low speed and low accuracy of power consumption prediction, and achieve the effect of improving accuracy and efficiency

Active Publication Date: 2017-07-07
QINGDAO CERAVI ELECTRONICS TECH CO LTD
View PDF7 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

With the continuous development of my country's social economy and the continuous adjustment of the industrial structure, the power consumption characteristics of power users are showing a trend of diversification. The diversification of power consum

Method used

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
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Smart community electricity consumption real-time prediction method and device based on depth learning
  • Smart community electricity consumption real-time prediction method and device based on depth learning
  • Smart community electricity consumption real-time prediction method and device based on depth learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0037] Such as figure 1 As shown, the present invention provides a method for real-time forecasting of electricity consumption in a smart community based on deep learning, comprising the following steps:

[0038] Step S10, constructing the topological structure of Storm, constructing a community power consumption database on the Storm platform, and designing a neural network for forecasting community power consumption;

[0039] Step S20, using the community electricity consumption database to train the neural network to obtain the optimal network model and weight matrix;

[0040] In the present invention, step S20 specifically includes the following steps:

[0041] Step S21, sending the power consumption data of each community to the bolt component under the Storm platform, and constructing a community power database for each community;

[0042] Step S22, input the data in the electricity consumption database of the community into the artificial neural network, and use the d...

Embodiment 2

[0051] A real-time prediction device for electricity consumption in smart communities based on deep learning, including:

[0052] The acquisition unit is used to obtain the historical power consumption data of the community to be predicted; the power consumption database construction unit is used to construct the power consumption data on the distributed real-time processing framework Storm; the convolutional neural network model construction unit , for training the convolutional neural network according to the power consumption data, and constructing a convolutional neural network model; the prediction unit, combined with the convolutional neural network model construction unit, according to the current electricity consumption of the community to be predicted for a certain period of time Quantity data to predict the future short-term electricity consumption of the community.

[0053] The working principle of the present invention is that the acquisition unit acquires the hist...

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

PUM

No PUM Login to view more

Abstract

The invention relates to a smart community electricity consumption real-time prediction method and device based on depth learning. The method comprises the steps of (1) obtaining the historical electricity consumption data of a community to be predicted, (2) constructing an electricity consumption database comprising the historical electricity consumption data in step (1) in a distributed real-time processing frame Storm, (3) using the electricity consumption database in the step (2) to train a convolutional neural network, and obtaining a convolutional neural network model, and (4) inputting the electricity consumption data of a community to be predicted of a current certain time period and obtaining a result of predicted electricity consumption amount. The method and the device have the advantages that the optimal weight of a community electricity consumption database is extracted through an artificial neural network, the accuracy of detection is effectively improved, on the basis of the Storm, the parallel community electricity consumption prediction is implemented, and a purpose of real-time prediction is achieved.

Description

technical field [0001] The invention relates to artificial intelligence technology, in particular to a method and device for real-time prediction of electricity consumption in a smart community based on deep learning. Background technique [0002] Deep learning is currently the highest level of machine learning development. As a method of deep learning, artificial neural network has a high effect in the fields of object recognition and image processing. For time series forecasting, artificial neural networks have the advantage of being able to automatically learn time series features, reduce manual intervention, and extract high-quality features. Since deep learning methods may consume a large amount of GPU resources, excessive calculations may not reach the actual Real-time effects in application scenarios. In order to solve the problem of real-time processing of big data, the distributed real-time processing framework Storm came into being. Storm has many application field...

Claims

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

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/04G06Q50/06G06N99/00G06N3/04
CPCG06N20/00G06Q10/04G06Q50/06G06N3/045
Inventor 张卫山张春峰孙浩云徐亮
Owner QINGDAO CERAVI ELECTRONICS TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap