BIM Parameter Set Selection for Accurate Building Energy Modeling
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Solution Overview
Problem
Conventional Building Information Modeling (BIM) technologies face challenges such as high costs, time consumption, and accuracy issues due to the need for extensive sensor data collection and manual processing, leading to inefficiencies in building energy management.
Innovation Solution
A method and apparatus for determining BIM information by detecting type-specific data, determining parameter ranges, generating parameter sets based on power consumption calculations, and optimizing these sets for accurate building energy modeling, reducing reliance on costly and error-prone manual processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If white-box modeling technology is used to achieve high accuracy in building energy modeling, then measurement precision is improved, but device complexity and cost increase due to requiring multiple sensors and measurement devices
Solution Approach 1:
The patent extracts only the essential parameters needed for accurate energy modeling from the comprehensive white-box approach, focusing on key operational data rather than all possible measurements. This selective extraction maintains accuracy while reducing sensor requirements and system complexity.
Solution Approach 2:
The patent uses copying by replicating parameter sets across similar building components or zones. Instead of measuring every parameter individually in every location, standardized parameter sets are copied and adapted, reducing the need for extensive sensor deployment while maintaining modeling accuracy.
2Measurement precision
If white-box modeling technology is used to achieve high accuracy, then measurement precision is improved, but time consumption increases due to requiring extensive data collection and manual processing
Solution Approach 1:
The patent applies preliminary action by pre-defining parameter sets and ranges based on building type and characteristics before actual data collection. This preparation work is done in advance, so that during implementation, only minimal data input is needed, significantly reducing time consumption while maintaining accuracy.
Solution Approach 2:
The system performs self-service through automated parameter determination and model generation. Once initial parameters are set, the system automatically generates complete parameter sets and creates the energy model without requiring manual intervention for each step, eliminating repetitive manual processes and reducing time consumption.
3Device complexity
If reference information based modeling technology is used to reduce cost and simplify the process, then device complexity is reduced, but measurement precision deteriorates due to large errors between actual and reference information
Solution Approach 1:
The patent applies local quality by customizing parameter sets according to specific building characteristics, operational patterns, and local conditions. Instead of using generic reference information for all buildings, the system adapts parameters to local qualities of each building, significantly improving accuracy while maintaining the simplicity of reference-based modeling.
Solution Approach 2:
The system introduces dynamics by making parameter sets adaptable and adjustable based on actual building performance data. The parameter sets are not fixed but can be dynamically refined and updated, allowing the model to evolve from static reference information to a dynamic, accuracy-improved system that maintains simplicity.
4Device complexity
If rate based modeling technology is used to simplify the process, then device complexity is reduced, but measurement precision deteriorates due to absolute factor range configuration and manual processes
Solution Approach 1:
The patent fundamentally changes the approach by moving from rate-based probabilistic methods to deterministic parameter-based modeling. Specific parameter values and ranges are defined based on building characteristics, eliminating the need for absolute factor ranges and probabilistic calculations, thereby improving accuracy while maintaining process simplicity.
Data Source
AI summary
The present disclosure relates to a sensor network, Machine Type Communication (MTC), Machine-to-Machine (M2M) communication, and technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the above technologies, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services.The present disclosure provides a method of determining information for building information modeling (BIM) by a BIM device. The method comprises detecting BIM data corresponding to type information related to a use characteristic of a building among predeterminded BIM data, determining a plurality of ranges for each of a plurality of parameters in the BIM data by using values for each of the plurality of parameters, generating multiple parameter sets based on the plurality of ranges for each of the plurality of parameters, and determining at least one of the multiple parameter sets as at least one parameter set to be used for the BIM, based on power consumption calculated for each of the multiple parameter sets.