Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Public energy consumption prediction method based on machine learning

A technology of energy consumption and machine learning, applied in machine learning, neural learning methods, forecasting, etc., can solve problems such as lack of effective means, and achieve high service quality, healthy environment, and accurate energy consumption prediction

Pending Publication Date: 2021-02-12
马鞍山学院
View PDF5 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the current situation that public energy consumption prediction still lacks effective means in the prior art, and proposes to provide a public energy consumption prediction method based on machine learning, mainly to solve how to integrate big data platform and machine learning into the management In the intelligent system of energy, the efficiency of the public sector is an important part of the concept of smart city. This invention is implemented in the existing MERIDA intelligent system to provide users with accurate energy consumption prediction

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
  • Public energy consumption prediction method based on machine learning
  • Public energy consumption prediction method based on machine learning
  • Public energy consumption prediction method based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] A kind of public energy consumption prediction method based on machine learning of the present embodiment, comprises the following steps:

[0053] S101. Data collection;

[0054]The big data collection method in the architecture, including data collection from the following three types of procedures: a, transfer construction, each public’s vitality, geospatial, static occupational attributes are obtained from the EMIS information system; b, used in the Internet of Things network SCADA automatically reads energy consumption sensors to collect energy consumption data and dynamic occupancy data; c. Collect environmental data in the network, including temperature, wind speed, air pressure, etc.

[0055] The above procedures performed within each building of the public sector at the national level can create big data, datasets stored in the cloud in high volume, variety and velocity.

[0056] S102. Data preprocessing, including:

[0057] A. Use the MAD algorithm to elimina...

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 discloses a public energy consumption prediction method based on machine learning, and belongs to the field of energy consumption prediction. The method comprises the following steps: S101, collecting data; S102, data preprocessing: A, performing outlier elimination by adopting an MAD algorithm; B, replacing the missing value; and C, performing variable reduction by adopting a PCA algorithm; and S103, carrying out prediction modeling; a DNN deep neural network is adopted for calculation, and the DNN and more hidden layers are used together in an R software tool in a Keras libraryby utilizing the collected data. The provided method overcomes the current situation that in the prior art, public energy consumption prediction still lacks effective means, and mainly solves the problem of how to integrate a big data platform and machine learning into an intelligent system for managing energy. Efficiency of a public department is an important component of a smart city concept, and the method is implemented in an existing MERIDA intelligent system and provides accurate energy consumption prediction for users.

Description

technical field [0001] The present invention relates to the technical field of energy consumption prediction, and more specifically, to a method for predicting public energy consumption based on machine learning. Background technique [0002] In the context of smart cities, how to achieve accurate energy consumption of public facilities is an important problem to be solved urgently, because large public buildings are the main energy consumers, especially public buildings with high frequency of use such as education, health, and government. For large buildings, an accurate energy consumption prediction model can effectively provide decision-making basis for energy consumption regulation and energy conservation optimization. However, the latest developments in machine learning in this field, the big data environment has not been fully utilized. [0003] After searching, the Chinese patent application number: 201811519685.5, and the name of the invention is: an energy consumpt...

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): G06Q10/04G06Q50/08G06N3/04G06N3/08G06N20/00
CPCG06Q10/04G06Q50/08G06N3/08G06N20/00G06N3/045
Inventor 林必毅苏聪郭伟郭晓明
Owner 马鞍山学院
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products