Automated Analytical Energy Model Generation from Conceptual Building Representations
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Solution Overview
Problem
The generation of analytical energy models for building design is complex and time-consuming, especially during the early stages, due to the manual processing of extensive information required for massing models, thermal zoning, and surface details.
Innovation Solution
A method that automatically generates an analytical energy model from a conceptual representation by deriving geometric information, assigning surface types, and defining thermal mass zones, allowing for quick energy analysis without the need for detailed manual specification at each level of the mass form model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing of extensive information is used for massing models, thermal zoning, and surface details, then the analytical energy model can be generated with high accuracy and completeness, but the process becomes complex and time-consuming
Solution Approach 1:
The system performs preliminary actions by automatically extracting geometric information from the conceptual representation before requiring user input for thermal zoning and surface details. The automated derivation of mass volumes and assignment of surface types occurs in advance, reducing the manual processing burden and time required during the model generation process.
Solution Approach 2:
The system enables self-service by automatically generating the analytical energy model from the conceptual representation without requiring extensive manual input. The automated processes include deriving geometric information, defining mass volumes, and assigning surface types, allowing the system to serve itself in generating the model while still requiring minimal user input for validation and refinement.
2Measurement precision
If detailed manual specification is provided for each level of the mass form model, then the analytical energy model can be generated with high accuracy, but the complexity of data entry and processing increases
Solution Approach 1:
The system segments the complex model generation process into distinct automated stages: extracting geometric information from the conceptual representation, deriving mass volumes, assigning surface types, and defining thermal zones. This segmentation allows each stage to be handled automatically with appropriate algorithms, reducing the overall complexity of data entry and processing while maintaining accuracy.
Solution Approach 2:
The system introduces intermediary processes that automatically translate the conceptual representation into the detailed analytical model. The automated derivation of mass volumes and assignment of surface types act as intermediaries between the high-level conceptual model and the detailed energy analysis requirements, eliminating the need for direct manual specification at every level and reducing processing complexity.
3Productivity
If automated generation of analytical energy model is implemented, then the process speed and productivity increase, but the system complexity and automation requirements increase
Solution Approach 1:
The system achieves universality by implementing a multi-functional automated platform that handles various tasks including geometric information extraction, mass volume derivation, surface type assignment, and thermal zone definition. This unified system performs multiple functions through integrated algorithms, increasing productivity while managing system complexity through cohesive design rather than separate specialized tools.
Solution Approach 2:
The system utilizes parameter changes by automatically adjusting and deriving various model parameters from the conceptual representation. The automated processes change parameters such as mass volume definitions, surface type assignments, and thermal zone characteristics based on the input geometry, enabling rapid model generation through algorithmic parameter transformation rather than manual specification.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a system, for generating an analytical energy model from a conceptual representation or a mass form model. In one aspect, actions include receiving a conceptual representation of a building including one or more user-defined floor levels and multiple surfaces defining volumetric space, and, responsive to a request to generate an analytical energy model, automatically generating the analytical energy model by: deriving geometric information from the conceptual representation; defining one or more mass volumes; algorithmically assigning one or more surface types to mass volume surfaces; defining one or more thermal mass zones based on the mass volumes and a corresponding number of user-defined floor levels; deriving thermal properties of the one or more thermal mass zones; and combining the defined one or more thermal mass zones, the derived thermal properties, and predefined analytical energy model parameters to generate the analytical energy model.