Collaborative energy management system
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Solution Overview
Problem
Existing energy management systems in multi-occupant spaces fail to comprehensively address individual comfort and energy efficiency, as they do not effectively account for the thermal correlations between zones and the varying preferences of occupants, leading to increased energy costs and discomfort.
Innovation Solution
A collaborative energy management system that uses adaptive learning to correlate temperature and energy flow measurements across zones, allowing occupants to input their comfort preferences and incorporating energy usage goals to optimize HVAC settings, while providing pricing signals for energy usage, thereby balancing energy cost and occupant comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If separate control of temperature in each zone is implemented, then individual occupant comfort can be improved, but energy consumption increases
Solution Approach 1:
The patent merges separate zone controls into a unified collaborative control system that considers thermal correlations between zones. The optimization system integrates temperature preferences from multiple occupants across different zones and coordinates HVAC settings across all zones simultaneously, rather than controlling each zone independently. This combining approach exploits thermal interactions between zones to reduce overall energy consumption while maintaining individual comfort preferences.
2Use of energy by moving object
If coordinated control of all thermal zones is implemented, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex coordinated control problem into manageable components: (1) an adaptive learning system that separately models thermal correlations for each zone pair, (2) an optimization system that processes occupant preferences zone-by-zone while considering inter-zone thermal effects, and (3) a modular architecture where each component performs a specific function. This segmentation reduces overall system complexity by breaking down the coordinated control task into independent, well-defined modules.
3Measurement precision
If adaptive learning of thermal correlations is implemented, then control accuracy is improved, but data processing requirements increase
Solution Approach 1:
The patent implements preliminary action by having the adaptive learning system continuously pre-compute and store thermal correlation models between zones during periods when full optimization is not needed. The system learns thermal relationships from historical data and stores these correlation models for rapid retrieval during real-time control. This preliminary computation of thermal correlations reduces the data processing burden during actual optimization cycles, as the complex thermal relationship calculations have already been performed and stored.
Data Source
AI summary
A collaborative energy management system, method and program product for a multi-zone space. A system is disclosed including: a plurality of environment sensors located throughout the multi-zone space; an adaptive learning system that collects environment data from the environment sensors and generates a correlation model that correlates historical environment data with HVAC settings; and an optimization system that utilizes the correlation model, inputted preferences received from a plurality of occupants within the multi-zone space, and energy usage goals to periodically generate new HVAC settings for controlling an HVAC system for the multi-zone space.