Smart Meter Data Compression for Load Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Smart meters face challenges in accurately identifying load conditions of electrical appliances due to inadequate computing capabilities, leading to low identification accuracy and increased bandwidth, storage, and calculation costs when uploading high volumes of power characteristic data to the cloud.
Innovation Solution
A management system that includes a smart meter and a remote server, where the smart meter compresses electricity data during loading events and uploads compressed data to the remote server for decompression and load identification, dynamically adjusting compression rates based on appliance types and cloud platform charging mechanisms to improve accuracy and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the smart meter enhances resolution to accurately obtain load condition of electrical appliances, then the identification accuracy is improved, but the amount of generated power characteristic data is increased, which increases bandwidth cost, storage space leasing cost and calculation cost
Solution Approach 1:
The patent extracts only the essential features needed for load identification by performing data compression on power characteristic data. The smart meter compresses the high-resolution data to retain identification-critical information while removing redundant details, thereby reducing the quantity of uploaded data without sacrificing identification accuracy.
Solution Approach 2:
The patent changes the parameter of data representation by applying compression algorithms that transform high-resolution power characteristic data into a more compact form. This parameter transformation maintains the essential identification features while significantly reducing data volume for cloud upload.
2Measurement precision
If the smart meter uploads high volume of power characteristic data to the cloud, then the load identification accuracy is improved, but the bandwidth cost, storage space leasing cost and calculation cost are increased
Solution Approach 1:
The patent extracts only the essential features needed for load identification by performing data compression on power characteristic data. The smart meter compresses the high-resolution data to retain identification-critical information while removing redundant details, thereby reducing the quantity of uploaded data without sacrificing identification accuracy.
Solution Approach 2:
The patent applies partial compression to data - compressing power characteristic data to the extent necessary for cost reduction while maintaining sufficient detail for accurate identification. This partial action approach avoids excessive compression that would harm identification accuracy.
3Loss of energy
If the smart meter performs data compression on electricity data, then the bandwidth cost and storage cost are reduced, but the computing capability requirement increases
Solution Approach 1:
The patent implements self-service by enabling the smart meter to autonomously perform data compression locally before uploading to the cloud. This self-service approach allows the smart meter to handle its own data optimization, reducing the computational burden on cloud servers and lowering overall system costs.
Solution Approach 2:
The patent performs data compression as a preliminary action at the smart meter before data transmission to the cloud. This preliminary processing reduces the data volume that needs to be transmitted and stored, thereby reducing bandwidth and storage costs while distributing computational work to the edge device.
Data Source
AI summary
A management system, a smart meter, a server, an operation method and a management method are provided. The management system includes a remote server and at least one smart meter. The smart meter is coupled to the remote server via a communication network. The smart meter measures electrical energy of at least one power line to obtain at least one batch of electricity data. The smart meter detects whether the loading event occurs. If the loading event occurs, the smart meter performs data compression on the electricity data obtained during an event period corresponding to the loading event to obtain compressed data, and uploads the compressed data to the remote server. The remote server performs data decompression on the compressed data to obtain decompressed data. The remote server performs load identification according to the decompressed data.