System for managing at least one comfort parameter of a building, calculator device and building system
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Solution Overview
Problem
Current building comfort management systems are inflexible and inefficient, relying on complex expert systems that struggle to account for variations in equipment performance and operational changes, leading to suboptimal energy consumption and operational costs.
Innovation Solution
A management system that utilizes operating diagrams stored in a computing device to optimize comfort parameters based on specific equipment operations, using a common communication protocol and variable optimization parameters like energy costs, to improve flexibility and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert systems with detailed building knowledge models are used to manage comfort parameters, then optimization precision is improved, but device complexity increases significantly
Solution Approach 1:
The patent segments the building management system into multiple hierarchical levels: local control units for individual zones/equipment and a central management system. This segmentation allows detailed optimization models to be distributed across multiple components rather than requiring a single complex global model, reducing overall system complexity while maintaining optimization precision at each level.
Solution Approach 2:
The system dynamically adapts the complexity of optimization models based on operational conditions. Simple rule-based control is used for routine operations, while more complex optimization algorithms are activated when beneficial. This dynamic approach allows the system to achieve high optimization precision when needed without permanently maintaining the complexity overhead of always-running complex models.
2Measurement precision
If expert systems with comprehensive building knowledge are implemented, then comfort parameter optimization is improved, but ease of operation deteriorates due to difficulty in modification
Solution Approach 1:
The system employs dynamic rule activation where optimization rules and parameters can be enabled or disabled based on current operational conditions, equipment availability, and user preferences. This allows the system to maintain high optimization precision through comprehensive models while remaining easily adaptable to changing conditions without requiring complete system reconfiguration.
Solution Approach 2:
The patent implements parameter-based control where optimization behavior is adjusted by changing numerical parameters and rule weights rather than restructuring the entire expert system. This allows operators to modify optimization precision and behavior by adjusting parameters in the central management system, maintaining both high optimization capability and operational flexibility.
3Ease of operation
If simple safety functions are used to prevent aberrant behaviors, then ease of operation is maintained, but productivity deteriorates due to suboptimal energy consumption
Solution Approach 1:
The patent introduces a central management system as an intermediary between simple local control units and the building equipment. The local units maintain operational simplicity while the central intermediary performs comprehensive optimization calculations, coordinating equipment operation across the entire building to achieve high energy efficiency without complicating individual equipment control.
Solution Approach 2:
The system replaces complex mechanical coordination between multiple equipment pieces with information-based coordination through the central management system. Rather than physically coupling equipment controls, the central system uses data communication and algorithmic optimization to achieve coordinated operation, maintaining simplicity at the equipment level while achieving high productivity through intelligent coordination.
Data Source
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AI summary
The system has a set of sensors (2) measuring a set of variables e.g. temperature variable (G1). A programmable controller (20) is arranged with a memory (21) for storing operation patterns (S). A reception and communication unit (25) is arranged for reception and communication of operation parameters to the memory. A calculating unit (22) is utilized for calculating control instructions (Cd) from measurement values (V1, V2) and optimizing an optimization criterion e.g. minimal production of carbon dioxide, based on the operation patterns.