Hierarchical Building Management with Agent-Based Scenario Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Building management systems (BMS) face challenges in efficiently managing energy consumption across various zones due to proprietary programming languages and manual sequencing of operations, leading to suboptimal energy usage and increased costs.
Innovation Solution
A hierarchical resource management system utilizing multifunctional software agents that monitor and control building systems, implementing machine learning algorithms to predict energy consumption and optimize scenarios for minimal energy use, while filtering out scenarios that violate internal laws of agents, thereby achieving target conditions with reduced energy expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual programming of sequences of operation is used in each controller, then device complexity is reduced and ease of operation is improved, but energy consumption increases and productivity decreases
Solution Approach 1:
The system enables self-service through autonomous agents that automatically monitor building conditions, generate operating scenarios, and adjust equipment operations without manual intervention. Each agent independently manages its controlled system by evaluating sensor data, selecting optimal scenarios, and commanding equipment adjustments, thereby reducing energy consumption while maintaining ease of operation.
Solution Approach 2:
The system implements continuous feedback loops where agents monitor sensed values from building equipment, compare actual performance against target conditions, and dynamically adjust operations. This feedback mechanism allows the system to learn from operational data, optimize energy consumption patterns, and automatically adapt to changing building conditions without increasing operational complexity.
2Device complexity
If proprietary programming languages are used for each device, then device complexity is reduced, but adaptability decreases and energy optimization is limited
Solution Approach 1:
The system achieves universality by implementing a standardized agent architecture that can interface with multiple types of building equipment (HVAC, lighting, security, etc.) through common communication protocols. Each agent serves multiple functions: monitoring sensor data, generating operating scenarios, validating scenarios against constraints, and commanding equipment adjustments. This multi-functional approach enables the system to adapt to different building systems without increasing individual device complexity.
Solution Approach 2:
The agent acts as an intermediary layer between the control system and proprietary building equipment. Rather than requiring direct integration with each device's proprietary programming language, the agent communicates through standardized interfaces while translating commands and data. This intermediary approach maintains device simplicity while enabling cross-platform adaptability and centralized energy optimization.
3Device complexity
If individual zone controllers operate independently, then device complexity is minimized, but overall energy consumption increases due to lack of coordination
Solution Approach 1:
The system merges independent zone controllers into a coordinated hierarchical structure where multiple agents work together under a central coordinator. While each agent maintains its controlled system for simplicity, the coordinator combines operating scenarios from multiple agents, validates them against building-wide constraints, and selects optimal combinations that minimize overall energy consumption. This merging approach reduces energy waste through coordinated operation without significantly increasing individual device complexity.
Data Source
AI summary
A hierarchical resource management system for a building includes one or more processors. The processors implement a plurality of agents that each monitor sensed values, and generate operating scenarios based on the sensed values for corresponding resources. The processors also implement a coordinator that filters the operating scenarios to remove the operating scenarios that violate internal laws of the agents to form an aggregate validated set of operating scenarios. The processors further implement a supervisor that, responsive to receipt of target conditions for the zones and the aggregate validated set of operating scenarios from the coordinator, selects a combination of the operating scenarios from the aggregate validated set of operating scenarios that achieves target conditions and minimizes overall energy consumption by the resources such that some of the operating scenarios of the combination do not minimize energy consumption of the resources corresponding to the some of the operating scenarios.