Group Preference Feedback for Building Temperature Control
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Solution Overview
Problem
Existing systems face challenges in efficiently collecting and aggregating individual preferences to optimize environmental conditions, such as temperature, in buildings, due to high transaction costs and the complexity of varying individual comfort levels, leading to over-cooling or over-heating issues.
Innovation Solution
A system comprising feedback devices that allow individuals to provide preference feedback in a single action, which is collected and processed by a computer to construct a group preference model, enabling real-time adjustments to environmental parameters like temperature to optimize comfort and reduce energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional survey methods are used to collect individual preferences, then preference information can be obtained, but transaction costs are high and response rates are low
Solution Approach 1:
The system enables individuals to automatically provide preference feedback through sensors and automated processes without requiring manual survey completion. Environmental sensors continuously monitor conditions and the system automatically adjusts settings based on aggregated preferences, eliminating the need for individuals to actively participate in surveys while still collecting comprehensive preference data.
Solution Approach 2:
The system implements continuous feedback loops where individual preferences are collected, aggregated into group preferences, and used to automatically adjust environmental conditions. This automated feedback mechanism replaces traditional survey methods by continuously gathering and acting on preference information without requiring repeated manual input from individuals.
2Ease of operation
If building environmental conditions are preset to a single temperature, then system operation is simple, but most occupants are uncomfortable and energy is wasted
Solution Approach 1:
The system transitions from static preset temperatures to dynamic, continuously adjustable environmental conditions. Environmental settings are automatically modified in real-time based on aggregated group preferences and sensor data, allowing the building to adapt to changing occupancy patterns and external conditions while maintaining simplicity for occupants.
Solution Approach 2:
The system changes environmental parameters (temperature, humidity, lighting) dynamically based on aggregated preference data. Instead of maintaining fixed preset values, the system continuously adjusts these parameters to match actual group preferences, reducing energy waste from over-conditioning while maintaining comfort.
3Loss of information
If individual preferences are collected and aggregated, then group preferences can be determined, but the process is complex and transaction costs are high
Solution Approach 1:
The system merges individual preference data into aggregated group preferences through automated processing. Multiple individual sensor inputs and preference signals are combined into a unified group preference profile that drives environmental control decisions, simplifying the complexity of handling individual data while maintaining accurate representation of group preferences.
Solution Approach 2:
The system introduces an automated intermediary layer (computing devices and algorithms) that mediates between individual preferences and group preference determination. This intermediary automatically collects, processes, and aggregates individual inputs without requiring manual intervention, reducing transaction costs and complexity while accurately deriving group preferences.
4Reliability
If buildings are over-cooled or over-heated to ensure comfort, then all occupants may be comfortable, but energy consumption increases significantly
Solution Approach 1:
The system applies partial conditioning only where and when needed based on actual occupancy and preference data. Instead of uniformly over-conditioning entire buildings, the system adjusts environmental settings in specific zones and times based on aggregated preferences, providing sufficient comfort while avoiding excessive energy consumption from unnecessary conditioning.
Data Source
AI summary
The present disclosed is directed to systems, methods, and devices for obtaining feedback information from individuals to reveal group preferences and to systems, methods, and devices for enabling providers to provide outcomes which utilize, at least in part, the preferences of the group. For example, a system comprising a plurality of devices, wherein at least one device of the plurality of devices captures at least one feedback in one substantially simple transaction; and the at least one device of the plurality of devices sends the at least one captured feedback to at least one computer; and the at least one computer receives the least one feedback; and the at least one feedback can be given at one or more of the following: periodic time intervals, predefined time intervals, random time intervals, substantially random time intervals and substantially any time.