Cloud-Edge Integrated Energy Optimizer to Reduce Data Transfer Latency
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Solution Overview
Problem
Existing building energy management systems face data transfer latency and IT security concerns, particularly in large buildings, which hinder real-time energy optimization and setpoint control.
Innovation Solution
The system splits optimization and control between an edge device for real-time data processing and a cloud for analytics, using model predictive control and big data analytics to optimize energy usage while distributing computational load and updating models simultaneously across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all energy optimization processing is performed in the cloud, then comprehensive analytics and model improvement are achieved, but data transfer latency and IT security risks increase
Solution Approach 1:
The system divides energy optimization processing into two segments: real-time data processing and control actions are performed locally at the building management system (edge device), while comprehensive analytics and model improvement are performed in the cloud. This segmentation allows the system to achieve both low-latency control and sophisticated analytics without the trade-off between them.
2Measurement precision
If all energy optimization processing is performed in the cloud, then comprehensive analytics are achieved, but IT security risks increase due to centralized data transfer
Solution Approach 1:
The system segments processing between cloud and edge, keeping sensitive building control data localized at the building management system while only transferring anonymized or aggregated data to the cloud for analytics. This maintains security by minimizing data exposure while still enabling comprehensive analytics.
Solution Approach 2:
The system implements local processing capabilities at the building management system, allowing it to autonomously perform real-time energy optimization decisions without requiring constant cloud communication. This local autonomy reduces security risks associated with centralized data transfer while maintaining analytical capabilities.
3Speed
If real-time control is implemented in large buildings, then energy optimization responsiveness improves, but data transfer complexity increases
Solution Approach 1:
The system segments the control architecture into local building management systems that handle real-time control autonomously and cloud-based systems that handle long-term analytics. This segmentation enables fast local responses without the complexity of real-time cloud communication, as the building management system makes immediate decisions based on local sensor data.
Data Source
AI summary
An integrated energy optimizer having an edge side and a cloud side. The edge side may incorporate an energy optimizer, a building management system connected to the energy optimizer, a controller connected to the building management system, and equipment connected to the controller. The cloud side may have a cloud connected to the energy optimizer and to the building management system, and a user interface connected to the cloud. Data from the field sensor may go to the optimizer and the building management system. The data may be processed at the optimizer and the building management system for proper settings at the building management system.