Semantic Building Models for Automated Smart Readiness Scoring
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Solution Overview
Problem
Current methods for assessing a building's smart readiness and automation capabilities are manual and time-consuming, requiring expert inspection and on-site evaluation, which hinders timely and efficient computation of key performance indicators like energy efficiency and automation levels.
Innovation Solution
An automated method that gathers data from building automation systems, creates semantic models, and uses performance templates to compute and expose automation capabilities, incorporating a guided user interface for uncertain services, aligning with the Smart Readiness Indicator (SRI) framework for machine-readable results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual expert inspection and on-site evaluation are used to assess building automation capabilities, then assessment accuracy and reliability are improved, but assessment time and operational complexity increase significantly
Solution Approach 1:
The patent creates a digital twin or virtual model of the building automation system that replicates the physical system's structure, components, and operational data. This digital copy can be assessed automatically without requiring physical on-site inspection, thus reducing assessment time while maintaining reliability through accurate data representation.
Solution Approach 2:
The patent introduces an automated assessment system that acts as an intermediary between the building automation system and the evaluation process. This intermediary automatically collects data from sensors, controllers, and building management systems, processes it through predefined criteria, and generates assessment results, eliminating the need for manual expert inspection while maintaining assessment accuracy.
2Measurement precision
If manual expert inspection methods are used, then detailed evaluation of building services is achieved, but the process becomes complex and difficult to standardize
Solution Approach 1:
The patent transforms the assessment process from a complex manual procedure into a standardized automated process by defining specific measurable parameters for each building service (heating, cooling, lighting, etc.). Each parameter has predefined measurement criteria and thresholds, allowing detailed evaluation to be conducted through automated data collection and comparison against standardized benchmarks.
Solution Approach 2:
The patent divides the building automation assessment into distinct modular components corresponding to different building services and domains. Each service (heating, cooling, ventilation, lighting, etc.) is assessed independently using standardized criteria, allowing the overall assessment to be composed of multiple simple, standardized evaluations rather than one complex manual process.
3Productivity
If automated computation methods are implemented, then assessment time is reduced and productivity increases, but the complexity of the automation system increases
Solution Approach 1:
The patent creates a universal automated assessment platform that can evaluate multiple building services and domains through a single integrated system. The platform uses standardized data collection methods, unified processing algorithms, and common output formats that work across different building types and automation systems, reducing the need for multiple specialized tools while maintaining comprehensive evaluation capabilities.
Solution Approach 2:
The automated assessment system is designed to autonomously collect data from building automation systems, process the information through predefined criteria, and generate assessment results without requiring external intervention. The system self-configures data collection parameters, automatically handles data processing, and produces standardized reports, thereby increasing productivity while keeping the operational complexity manageable through automation.
Data Source
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AI summary
The present invention discloses a Method for automatically computing the automation capabilities of a building; said method comprises the steps of: i) providing a plurality of SRI calculation rules and creating a performance semantic model from said plurality of SRI calculation rules; ii) creating a building sematic model and retrieving mappings between the performance semantic model and the building semantic models and optionally adding information about building's documentation to the retrieved mappings; iii) providing a building digital representation for the existing building, said building digital representation being in the form of a semantic building model; iv) computing a partial performance model for the retrieved matches by using the available digital building representation to automatically retrieve building's capabilities that are relevant for the SRI computation; iv) providing a guided user interface for completing the computation for building services that are not found in the digital building representation; and v) computing and reporting the SRI calculation, the scores at the different impact criteria and preferably as well as a justification for each score. Thus, the present invention paves a way allowing for automatically computing the capabilities of a building in terms of automation and autonomy additionally guiding a Smart Readiness Indicator (SRI) score assessment for non-digitally available information (e.g. documents/schemas/plans) which finally leads to complete SRI computation for existing buildings. Since the method now facilitates to automatically compute scores for at least a subset of the services, a system can periodically recompute the performance of a large number of buildings and can offer an actual status of a fleet of buildings in terms of automation and autonomy. The guide for experts helps to compute the final SRI scores of buildings by helping in finding the information for the missing scores which results in a significant reduction in the time needed for getting a clear picture of the building performance.