Remote automated deployment of HVAC optimization software
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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 computer-implemented method for deploying HVAC optimization software that includes automatic data collection, generation of customized energy optimization programming, and automatic deployment using a standard operating control platform in an energy optimization control engine (EOCE) computing system communicatively coupled to a building automation system (BAS), which receives data sets identifying HVAC components, operational control parameters, and measured operations data to generate an energy optimized operating control platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data collection and software programming are used for HVAC optimization system deployment, then customization and reliability are improved, but deployment time and cost increase significantly
Solution Approach 1:
The patent uses template-based software programming that copies and adapts pre-configured optimization routines for different HVAC equipment types. Instead of manually programming each system from scratch, the system uses standardized templates that can be automatically instantiated and customized through parameter selection, dramatically reducing deployment time while maintaining reliability through proven optimization algorithms.
Solution Approach 2:
The patent performs preliminary data collection and system characterization during the manufacturing or installation phase, storing equipment specifications and operational parameters in a database. This preliminary action enables the optimization software to be deployed quickly by simply retrieving and applying pre-collected data, eliminating the need for time-consuming on-site data collection while ensuring accurate system customization.
2Reliability
If manual deployment processes are used for HVAC optimization software, then system accuracy and reliability are improved, but deployment cost and complexity increase
Solution Approach 1:
The patent implements self-service deployment where the optimization software automatically configures itself by reading equipment data from building automation systems, selecting appropriate optimization templates, and deploying configurations without manual intervention. This self-service approach maintains accuracy through automated validation routines while dramatically simplifying the deployment process and reducing the need for specialized technicians.
Solution Approach 2:
The patent replaces manual mechanical deployment processes with automated electronic systems. Instead of technicians physically installing and configuring software on-site, the system uses automated data exchange protocols, electronic template instantiation, and remote deployment mechanisms to activate optimization software, reducing both complexity and the need for manual labor while maintaining reliability through automated validation.
3Reliability
If extensive manual intervention is used in HVAC optimization deployment, then system performance and reliability are improved, but deployment speed and frequency of updates decrease
Solution Approach 1:
The patent enables continuous deployment and updating of optimization software by automating the entire deployment pipeline. The system can continuously collect equipment data, automatically generate optimized configurations using templates, and deploy updates without interruption to HVAC operations. This continuous action allows frequent updates and optimizations while maintaining system performance through automated validation and rollback capabilities.
Solution Approach 2:
The patent uses copyable template configurations that can be rapidly instantiated and deployed across multiple HVAC systems. These standardized templates ensure consistent, reliable optimization performance while enabling rapid deployment and frequent updates across different equipment without requiring extensive manual customization for each system, thereby increasing deployment frequency while maintaining performance standards.
Data Source
AI summary
Computer-implemented methods and structures deploy a heating ventilation and air conditioning (HVAC) energy optimization program. A standard operating control platform (OCP) is deployed in an energy optimization control engine (EOCE) computing system communicatively coupled to a plurality of HVAC components via a building automation system (BAS). An energy optimization portal (EOP), which receives from the EOCE computing system a first data set identifying the plurality of HVAC components, a second data set including operational control parameters for each of the plurality of HVAC components, and a third data set including measured operations data associated with each of the plurality of HVAC components. The EOP generates an energy optimized operating control platform based on the first, second, and third data sets, which is automatically communicated from the EOP to the EOCE computing system.