Cloud and edge integrated energy optimizer
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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 the optimization process between an edge device for real-time control and data collection, and a cloud for analytics and model improvement, using model predictive control (MPC) to minimize energy costs while maintaining comfort levels, with data transfer and model updates optimized for reduced latency and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all optimization computation is performed on the cloud, then comprehensive analytics and model improvement are achieved, but data transfer latency and control response time increase
Solution Approach 1:
The system divides the optimization computation into two segments: real-time control optimization performed locally on edge devices (premises energy optimizers) and comprehensive analytics/model improvement performed on the cloud. This segmentation allows time-critical control decisions to be made locally without cloud communication delays, while still benefiting from cloud-based analytical capabilities for model enhancement.
2Extent of automation
If setpoint changes are controlled from the cloud, then centralized control is achieved, but IT security risks and control latency increase
Solution Approach 1:
The system implements different control qualities at different locations: local autonomous control capability at premises energy optimizers for immediate response and security, with cloud-based oversight for strategic optimization. This allows setpoint changes to be initiated locally without requiring constant cloud authorization, reducing both latency and security exposure while maintaining centralized analytical supervision.
3Productivity
If real-time optimization is implemented, then energy cost savings improve, but data transfer requirements and system complexity increase
Solution Approach 1:
The premises energy optimizers are designed to autonomously perform real-time optimization computations locally without requiring continuous cloud intervention. They self-manage control decisions based on local sensor data and pre-loaded models, reducing system complexity and data transfer requirements while maintaining high optimization efficiency. The cloud serves primarily for initial model distribution and periodic updates rather than continuous control.
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.