Building power management systems
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Solution Overview
Problem
Current building power management systems are inefficient due to static and coarse-grained control methods, incompatible with real-time and spatial variations in building conditions, leading to high energy consumption and environmental impact.
Innovation Solution
An integrated building power management system with a hierarchical cyber-physical architecture, utilizing software layers to dynamically manage power distribution based on real-time data from sensors and occupant preferences, enabling fine-grain power management through multiple states and optimized resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If static and coarse-grained control methods are used, then device complexity is reduced, but energy efficiency deteriorates
Solution Approach 1:
The control system is divided into multiple hierarchical layers (building level, zone level, device level) with each layer managing specific control functions. This segmentation allows complex energy optimization to be achieved through distributed, modular control rather than a single monolithic system, resolving the contradiction between energy efficiency and system complexity.
Solution Approach 2:
The system transitions from static control to dynamic control by continuously adjusting power states based on real-time sensor data, occupancy patterns, and environmental conditions. Multiple power states (off, standby, active, over-active) enable fine-grained dynamic adjustment of energy consumption while maintaining comfort, thereby improving energy efficiency without excessive complexity.
2Productivity
If fine-grain power management with multiple states is implemented, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The system adds a temporal dimension to power management by implementing multiple power states that transition over time based on occupancy and environmental conditions. This dimensional approach (adding time-based state transitions) enables fine-grain energy management without proportionally increasing spatial or structural complexity, as the complexity is managed through temporal logic rather than physical complexity.
Solution Approach 2:
The system changes the parameter of power consumption by implementing multiple discrete power states (off, standby, active, over-active) rather than binary on/off control. This parameter transformation enables nuanced energy management where each state represents a different power consumption level, improving energy efficiency while the hierarchical architecture manages the complexity of coordinating these states across multiple devices.
3Adaptability or versatility
If real-time dynamic control based on sensor data is used, then adaptability to building conditions is improved, but device complexity increases
Solution Approach 1:
The system implements continuous feedback loops where sensors monitor environmental conditions (temperature, humidity, occupancy) and this data feeds back to the control system which adjusts power states accordingly. This feedback mechanism enables real-time adaptability to changing building conditions while the hierarchical architecture organizes the feedback processing to manage complexity through distributed decision-making at each control level.
Solution Approach 2:
The hierarchical control architecture serves multiple functions simultaneously: it manages power state transitions, processes sensor data, coordinates across different zones and devices, and optimizes energy consumption. This multi-functionality reduces the need for separate specialized systems for each control task, thereby improving adaptability without proportionally increasing overall system complexity.
Data Source
AI summary
Methods, systems, and devices are disclosed for managing building power, in one aspect, a method for managing building power includes determining values for power usage of a heating, ventilation, or air conditioning (HVAC) system in one or more zones of a building, the values including a cost of power value, a comfort value, a weighting function between the cost of power value and the comfort value, or a thermal storage value, in which the determining the values is based on a plurality of parameters including a price of power, a time of use, a total power allocation, or random variables including weather and building occupancy factors, and determining a power level for a plurality of states based on the determined values, the plurality of states corresponding to different levels of power to operate the HVAC system in the one or more zones.