Controlling HVAC optimization using real time data
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Solution Overview
Problem
The conventional process of installing and activating HVAC energy optimization control systems is expensive and time-consuming, requiring substantial onsite manual data collection and offsite software programming, followed by manual deployment at the building site.
Innovation Solution
A distributed computing system involving local building automation servers, a cloud-based network, and client terminals for real-time HVAC monitoring and control, using client-level virtual machines with background and foreground sub-processes to collect, analyze, and present data, allowing for remote configuration and optimization of HVAC systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual data collection and software programming is used for HVAC optimization, then system configuration and control capabilities are achieved, but installation time and cost increase substantially
Solution Approach 1:
The patent uses virtual machines as software copies that replicate HVAC control functionality without requiring physical hardware installation or manual configuration. The virtual machine contains pre-configured optimization algorithms and can be deployed digitally, eliminating traditional onsite data collection and software programming steps.
Solution Approach 2:
The virtual machine automatically performs HVAC optimization control functions without requiring manual intervention for configuration or programming. The system self-configures upon deployment, automatically collecting necessary data and implementing optimization strategies, thereby eliminating the need for substantial manual setup time.
2Reliability
If conventional manual data collection and software programming is used for HVAC optimization, then system configuration and control capabilities are achieved, but labor costs and complexity increase
Solution Approach 1:
The virtual machine serves as a portable, pre-configured software copy that encapsulates all necessary optimization logic and control algorithms. This single deployable unit replaces complex manual configuration processes, reducing both labor costs and installation complexity while maintaining full optimization capability.
Solution Approach 2:
The virtual machine is designed as a universal platform that can be deployed across different HVAC systems without requiring system-specific manual programming. It automatically adapts to various configurations, eliminating the need for specialized software development for each installation and thereby reducing overall system complexity.
3Productivity
If real-time data collection and analysis is implemented, then energy optimization efficiency improves, but data processing requirements and system complexity increase
Solution Approach 1:
The virtual machine acts as an intermediary layer between raw HVAC operational data and optimization decisions. It contains pre-built data processing pipelines and analysis algorithms that automatically handle real-time data collection, processing, and interpretation, enabling efficient optimization without requiring complex external data processing infrastructure.
Solution Approach 2:
The patent merges data collection, data analysis, and optimization control functions into a single integrated virtual machine platform. This consolidation eliminates the need for separate complex systems for each function, reducing overall system complexity while maintaining real-time processing capability for energy optimization.
Data Source
AI summary
The present disclosure describes a solution to monitor, control and share HVAC operation state information and the analysis thereof based on a distributed computing system involving local building automation servers (BAS), a network based (cloud-based) system and client terminals. On the network based system, a client level virtual machine launches a container process for a client, which includes a background sub-process and a foreground sub-process. The background sub-process collects and analyzes HVAC data without client interaction. The foreground sub-process is setup upon the launching of the container process, but is fully activated until suitable client interaction is detected. The foreground sub-process pushes for a more comprehensive set of HVAC operation data through the BAS server and analyzes and presents the data and analysis result in substantially real time to the client through the client terminal with a higher data updating rate than the background sub-process.