Cloud-Based Asset Setpoint Control Under Comfort Constraints
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Solution Overview
Problem
Asset settings configured by user knowledge or written instructions often result in inefficiencies and decreased performance due to suboptimal sensor locations, energy losses, and continuous demand, leading to reduced performance and energy efficiency.
Innovation Solution
An automated setpoint generation system via cloud-based supervisory control that adjusts settings based on comfort constraints and energy optimization, using regression analysis and decision trees to optimize asset performance and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If asset settings are configured by user knowledge or written instructions, then configuration is simple and quick, but asset performance and energy efficiency deteriorate due to suboptimal settings
Solution Approach 1:
The system enables automated self-configuration of asset settings through cloud-based supervisory control. The automated asset strategy selector and setpoint generator automatically determine optimal strategies and adjust settings without user intervention, allowing the system to serve itself rather than relying on manual user configuration. This resolves the contradiction by maintaining operational simplicity while dramatically improving asset performance through algorithmic optimization.
Solution Approach 2:
The system dynamically changes operational parameters (setpoints) based on real-time conditions and learned patterns. The automated setpoint generator continuously adjusts asset settings to optimize performance, transforming static user-configured parameters into dynamic optimized values. This resolves the contradiction by maintaining ease of initial configuration while achieving superior performance through continuous parameter optimization.
2Ease of manufacture
If manual configuration methods are used, then implementation is straightforward, but energy efficiency and performance deteriorate due to continuous demand and suboptimal settings
Solution Approach 1:
The system implements closed-loop feedback where the automated asset strategy selector continuously monitors asset performance and environmental conditions, then adjusts strategies and setpoints accordingly. This feedback mechanism enables the system to learn from past performance and continuously improve energy efficiency while maintaining straightforward implementation through automated cloud-based control. The feedback loop resolves the contradiction by transforming static manual configuration into dynamic optimized control without increasing implementation complexity.
3Productivity
If automated cloud-based supervisory control is implemented, then asset performance and energy efficiency improve, but system complexity and processing requirements increase
Solution Approach 1:
The system introduces a cloud-based automated asset strategy selector as an intermediary between the user and the asset control system. This intermediary handles the complex tasks of strategy selection and setpoint generation remotely, allowing local assets to achieve optimized performance without requiring complex local processing infrastructure. The cloud-based intermediary resolves the contradiction by centralizing computational complexity while maintaining simple local implementation.
Data Source
AI summary
Various embodiments described herein relate to automated setpoint generation for assets via cloud-based supervisory control. In this regard, a request to perform supervisory control with respect to an asset is received. The request comprises an asset identifier indicating an identity of the asset. In response to the request, one or more setpoints for the asset is determined based on the asset identifier. Also in response to the request, comfort constraint data indicative of one or more comfort constraints for an environment associated with the asset is determined based on the asset identifier. Furthermore, the one or more setpoints for the asset is adjusted based on the comfort constraint data.