Hierarchical Building Control Using Agent-Based Energy Scenarios
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Building management systems (BMS) face challenges in efficiently managing energy consumption across diverse building systems 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 with multifunctional software agents that monitor and control building systems, using machine learning algorithms to predict energy consumption and optimize scenarios for minimal energy use, while filtering scenarios to comply with internal laws and constraints of individual agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual programming of sequences of operation is used in each controller, then proprietary programming can be implemented, but energy optimization is suboptimal and programming complexity increases
Solution Approach 1:
The patent introduces a centralized energy management system that acts as an intermediary between individual building controllers and the central control. This mediator collects data from multiple controllers, performs global optimization calculations, and distributes optimized setpoints back to individual controllers, thereby reducing the programming complexity at each device while achieving system-wide energy optimization.
Solution Approach 2:
The energy management system serves multiple functions: it monitors energy consumption across all building systems, optimizes operational sequences, coordinates between different controllers, and provides reporting. This multi-functional approach consolidates what would otherwise require complex proprietary programming in each individual controller into a single universal system.
2Measurement precision
If individual zone controllers operate independently with proprietary programming, then each zone can be controlled precisely, but overall energy consumption cannot be optimized across the entire building
Solution Approach 1:
The patent merges individual zone control decisions with central building-wide energy optimization. The system combines local zone requirements with global energy considerations by having the central energy management system coordinate setpoints across multiple zones, allowing precise zone control to be maintained while achieving overall energy optimization through coordinated operation.
Solution Approach 2:
The system implements feedback loops where the central energy management system continuously monitors energy consumption data from individual zone controllers, analyzes the impact of local control decisions on overall building energy use, and adjusts setpoints accordingly. This feedback mechanism enables the system to maintain zone precision while optimizing total energy consumption.
3Adaptability or versatility
If manual sequencing of operations is used, then system operation can be customized, but energy costs increase and operational efficiency decreases
Solution Approach 1:
The patent implements dynamic optimization where the energy management system continuously adjusts operational sequences based on real-time conditions such as occupancy, weather, and energy prices. Rather than using static manual sequences, the system dynamically generates optimized schedules that adapt to changing conditions, maintaining customization while reducing energy costs through automated optimization algorithms.
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.