Occupant Feedback Control for Group-Based Building Temperature Settings
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Solution Overview
Problem
Existing systems face challenges in efficiently collecting and aggregating individual preferences to optimize environmental conditions in buildings, leading to over-cooling and over-heating, which increases energy consumption and reduces occupant comfort due to high transaction costs associated with traditional feedback methods.
Innovation Solution
A system and method that deploy feedback devices to collect preference feedback in real-time, aggregate data using fuzzy variables, and adjust environmental conditions like temperature to maintain or move group preferences within a certain range, utilizing a network of sensors and processors to minimize transaction costs and improve comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional feedback methods (surveys, paperwork, computer forms) 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 implements continuous automated feedback collection through sensors and digital interfaces that monitor environmental conditions and automatically gather preference data from occupants. This eliminates manual survey processes and creates an ongoing feedback loop where preferences are collected, aggregated, and used to adjust environmental settings automatically, significantly reducing transaction costs and time requirements.
Solution Approach 2:
The system enables occupants to provide preference feedback through simple self-service mechanisms such as digital buttons, mobile applications, or automated sensors that detect occupancy and environmental conditions. This self-service approach eliminates the need for manual survey administration and processing, allowing individuals to contribute preference data effortlessly while the system automatically aggregates and processes the information.
2Ease of operation
If building environmental conditions are preset to a single temperature, then system operation is simple, but most buildings are over-cooled or over-heated leading to energy waste and occupant dissatisfaction
Solution Approach 1:
The system transitions from static preset temperature control to dynamic environmental adjustment based on real-time aggregated group preferences. Sensors continuously monitor environmental conditions and occupancy, while the system automatically adjusts temperature, lighting, and other environmental parameters to match current group preferences, eliminating both over-cooling and over-heating while maintaining simple operation through automation.
Solution Approach 2:
The system dynamically changes environmental parameters such as temperature, humidity, and lighting levels based on aggregated group preferences rather than maintaining fixed preset values. This parameter adjustment is driven by continuous feedback from occupants and automated sensors, allowing the building to adapt to changing conditions and preferences while optimizing energy consumption.
3Reliability
If individual preferences are collected and aggregated to form group preferences, then occupant comfort can be improved, but the complexity of collecting and processing individual data increases
Solution Approach 1:
The system merges individual preference data into aggregated group preferences using automated processing algorithms. Multiple individual inputs from various occupants are combined and processed to determine the collective group preference, which is then used to adjust environmental conditions. This merging process simplifies the complexity by automatically aggregating data rather than requiring manual analysis of individual responses.
Solution Approach 2:
The system introduces an intermediary automated processing layer between individual preference inputs and environmental control adjustments. This intermediary component aggregates individual data points, processes them through algorithms to determine group preferences, and translates them into environmental settings, thereby reducing the complexity of directly managing individual data while improving reliability of comfort outcomes.
Data Source
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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.