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 optimization and control between an edge device for real-time data processing and a cloud for big data analytics, allowing for reduced latency and improved model updates, using model predictive control and big data analytics to optimize energy usage while maintaining comfort levels.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If data is transferred from building management system to cloud for optimization, then energy optimization can be performed, but data transfer latency increases and real-time control is hindered

Engineering Contradiction:

Improveenergy optimizationVSAvoiddata transfer latency

Core Design Contradiction:

Loss of energyVSLoss of time

Solution Approach 1:

The system segments the energy management functionality into two parts: a cloud-based optimization server that performs big data analytics and model updates, and an edge device that executes real-time control decisions locally. This segmentation allows energy optimization calculations to be performed in the cloud without requiring continuous data transfer for control actions, thus reducing latency while maintaining optimization benefits.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The edge device acts as an intermediary between the building management system and the cloud optimization server. It receives optimization models and parameters from the cloud, processes local sensor data, and executes control decisions without needing to transfer every data point to the cloud, thereby reducing data transfer latency while still enabling energy optimization.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of energy

If cloud-based optimization is used, then energy management can be improved, but IT security concerns arise due to remote setpoint changes

Engineering Contradiction:

Improveenergy management efficiencyVSAvoidIT security

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The system implements local quality by enabling the edge device to autonomously execute control decisions based on locally stored optimization models, without requiring continuous cloud connectivity or remote setpoint changes. This local autonomy reduces IT security risks while maintaining energy management effectiveness.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The cloud optimization server performs preliminary calculations to generate optimization models and parameters, which are then downloaded to the edge device. This preliminary action allows the edge device to make real-time control decisions without needing to contact the cloud for each decision, reducing both latency and security risks associated with remote setpoint changes.

Inventive Principle:

Principle #10Preliminary action

3Extent of automation

If all optimization calculations are performed in the cloud, then centralized control is achieved, but computational load and data transfer requirements increase

Engineering Contradiction:

Improvecentralized optimization controlVSAvoidcomputational load distribution

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The computational workload is segmented between the cloud and edge device. The cloud performs heavy computational tasks for generating optimization models and performing big data analytics, while the edge device handles real-time data processing and control execution. This segmentation reduces the computational burden on any single system and minimizes data transfer requirements.

Inventive Principle:

Principle #1Segmentation

4Speed

If real-time control is implemented, then responsiveness is improved, but data transfer latency prevents optimal performance

Engineering Contradiction:

Improvecontrol responsivenessVSAvoiddata transfer latency

Core Design Contradiction:

SpeedVSLoss of time

Solution Approach 1:

The edge device serves as an intermediary that enables real-time control by processing local sensor data and executing control decisions without requiring continuous cloud communication. It only needs to periodically receive updated optimization models from the cloud, significantly reducing data transfer latency while maintaining control responsiveness.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

Optimization models and control parameters are pre-calculated and downloaded to the edge device before real-time control is needed. This preliminary action enables the edge device to execute rapid control responses based on locally available information, eliminating data transfer latency for control decisions.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11499734B2Cloud and edge integrated energy optimizer

Publication Date: 2022.11.15 HONEYWELL INTERNATIONAL INC

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.