HVAC Analytic Rule Mapping for Bulk BAS Data Tagging
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Solution Overview
Problem
Current Building Automation Systems (BAS) do not optimize HVAC energy usage in real time, requiring manual processing of thousands of raw data points, which is time-consuming and inefficient, taking months to achieve optimal energy management.
Innovation Solution
An energy management system that integrates with BAS, automatically processes data points by uploading them to the cloud, filtering, and bulk tagging equipment, applying pre-stored analytical rules to generate reports without user-specific knowledge, enabling real-time energy usage optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing of raw data points is used, then data can be processed with existing systems, but processing time is extremely long (months)
Solution Approach 1:
The patent pre-processes and stores analytical rules in a cloud-based repository before they are needed for data processing. This preliminary action allows the system to quickly retrieve and apply pre-configured rules during bulk processing, eliminating the need to manually create and configure rules for each data point, thereby reducing processing time from months to hours
Solution Approach 2:
The patent creates standardized templates and copies of analytical rules that can be automatically applied to multiple equipment types. Instead of manually processing each unique data point with custom rules, the system uses copied rule templates that can be bulk-applied across thousands of data points simultaneously, dramatically increasing processing efficiency
2Ease of manufacture
If manual processing of data points is used, then existing BAS systems can be utilized, but the process is extremely time-consuming and inefficient
Solution Approach 1:
The patent creates a universal cloud-based platform that can process data from multiple BAS system vendors simultaneously. The system uses standardized equipment hierarchies and analytical rules that work across different BAS systems, eliminating the need for vendor-specific manual processing procedures while maintaining ease of integration with existing systems
Solution Approach 2:
The patent introduces a cloud-based intermediary platform that sits between the BAS system and the energy management analysis. This intermediary automatically receives raw data points, applies pre-configured analytical rules, and generates processed results, serving as a mediator that handles the complex processing tasks while keeping the user interface simple and maintaining compatibility with existing BAS systems
3Adaptability or versatility
If cryptic character strings are used to identify equipment, then vendor-specific naming conventions are maintained, but the data must be deciphered manually which is time-consuming
Solution Approach 1:
The patent implements automatic equipment identification where the cloud-based system self-services by automatically deciphering and mapping cryptic character strings from various BAS vendors. The system uses automated parsing algorithms that recognize vendor-specific naming conventions and automatically translate them into standardized equipment hierarchies without requiring manual intervention, thereby maintaining vendor compatibility while eliminating manual deciphering
Solution Approach 2:
The patent transforms cryptic character strings into standardized equipment parameters through automated parameter mapping. The system changes the state of raw data by converting vendor-specific string formats into standardized equipment identification parameters, enabling automatic recognition and processing while maintaining compatibility with multiple vendor naming conventions
4Measurement precision
If one data point is processed at a time, then detailed individual analysis is possible, but processing thousands of points takes months
Solution Approach 1:
The patent merges individual data point processing into bulk batch operations. The cloud-based system combines thousands of data points into batches and applies analytical rules to entire batches simultaneously rather than processing points sequentially. This merging maintains the precision of individual analysis while achieving the throughput of bulk processing through parallel computation and optimized batch operations
Data Source
AI summary
An energy management system is disclosed for optimizing energy usage of HVAC equipment in a building complex. The energy management system is configured to be integrated into an existing Building Automation System (“BAS system”) in order to process the data points in a less time consuming and efficient manner relative to known systems that map one point at a time. The BAS system data points are “point mapped”, i.e., uploaded to a file in the “cloud”, and are updated continuously as a function of time and deposited in a “bucket” in which the data points are unfiltered. These data points can then be filtered by node path and equipment in order to bulk tag equipment and bulk tag points in each of the buildings. These bulk tagged points data points can then be linked to specific rules in an analytical rules library. The system automatically applies predetermined analytical rules to tagged HVAC data points without specific knowledge of the rule by the user. These analytical rules are used to determine energy usage for each type of equipment and are pre-stored in the cloud. By selecting an equipment type, the correct analytical rule is automatically applied in bulk to the selected HVAC equipment type, and a report may be selectively generated for the selected piece(s) of HVAC equipment.