Hierarchical Building Control for Cross-Zone Energy Coordination
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Solution Overview
Problem
Building management systems (BMS) face challenges due to proprietary programming languages and manual sequencing, leading to inefficiencies in controlling and monitoring building equipment, which can result in suboptimal energy consumption and cost levels.
Innovation Solution
A hierarchical resource analysis system with SMITHGROUP-AI, comprising processors, causal agents, and a causal coordinator, that analyzes environmental and resource states to optimize energy consumption and cost by dynamically adjusting system operations based on predefined ranges and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If proprietary programming languages and manual sequencing are used in BMS, then device compatibility and control capability are improved, but system complexity and operational efficiency deteriorate
Solution Approach 1:
The patent introduces a standardized communication protocol as an intermediary layer between diverse building equipment and the central management system. This protocol translates proprietary device-specific commands into a universal language, enabling compatibility across different manufacturers and device types without requiring complex proprietary programming at each device level.
Solution Approach 2:
The system implements a universal control interface that can manage multiple types of building equipment (HVAC, lighting, security, etc.) through a single standardized protocol. This multi-functional approach eliminates the need for device-specific programming languages while maintaining full control capability across diverse equipment types.
2Adaptability or versatility
If proprietary programming languages and manual sequencing are used in BMS, then device compatibility and control capability are improved, but energy consumption and operational cost increase
Solution Approach 1:
The system enables building equipment to automatically monitor and adjust their own operations based on real-time environmental data and pre-set parameters. Devices self-regulate without requiring continuous manual intervention or complex centralized control, reducing the energy consumption of the management system while maintaining device compatibility through the standardized protocol.
Solution Approach 2:
The control system dynamically adjusts equipment operations based on real-time conditions (occupancy, weather, time-of-day) rather than relying on static proprietary programming. This dynamic adaptation optimizes energy consumption automatically while the standardized protocol ensures seamless communication across different device types.
3Manufacturing precision
If manual programming is used for each controller, then precise control sequences are achieved, but productivity and response time deteriorate
Solution Approach 1:
The system pre-configures control sequences and parameters during installation using the standardized protocol, storing optimized control logic in each device's memory. This preliminary configuration enables devices to execute precise control actions automatically in real-time without requiring manual programming adjustments, maintaining control precision while dramatically improving system responsiveness and productivity.
Data Source
AI summary
A hierarchical resource analysis system, for a building that has a plurality of zones each with a corresponding resource arranged to alter an environment of the zone, includes one or more processors that implement a plurality of causal agents and a causal coordinator. Each of the causal agents reports to the causal coordinator parameter values describing a state of the environment of one of the zones and parameter values describing a state of the corresponding resource for the zone. The causal coordinator, responsive to indication that at least one of the parameter values describing a state of the environment of one of the zones is outside a predefined zone range and all of the parameter values describing the states of the corresponding resources for the zones being within corresponding predefined resource ranges, commands at least one of the causal agents to operate the corresponding resource within an altered span.