Social Learning Thermostat for Comfort-Energy Setpoint Arbitration
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Solution Overview
Problem
Commercial building facility managers face challenges in balancing occupant comfort and energy savings due to lack of knowledge on individual comfort preferences, leading to either energy sacrifices or over-reaction to complaints, resulting in inefficiencies and discomfort.
Innovation Solution
A software-based system, the Collaborative Energy and Comfort Control (CECC) platform, which includes a social learning thermostat (softThermostat) that arbitrates temperature settings across locations in a building based on occupant preferences and energy policies, using a processor to minimize temperature differences and optimize energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If facility managers implement aggressive energy policies to achieve energy savings, then energy consumption is reduced, but occupant comfort is significantly sacrificed
Solution Approach 1:
The system segments the building into multiple thermal zones with individual temperature control, allowing different occupants in different zones to have their comfort preferences met independently while the overall system optimizes energy consumption across all zones
Solution Approach 2:
The system dynamically adjusts temperature setpoints based on real-time occupancy detection, outdoor conditions, and learned occupant preferences, transitioning from static aggressive energy policies to adaptive control that maintains comfort when needed and saves energy when possible
2Object-affected harmful factors
If facility managers relax energy policies to avoid occupant complaints, then occupant comfort is improved, but energy savings opportunities are reduced
Solution Approach 1:
The system implements feedback loops where occupancy sensors, temperature sensors, and occupant preference inputs continuously inform the control algorithm, which adjusts setpoints to maintain comfort while avoiding unnecessary energy consumption through data-driven decisions
Solution Approach 2:
The system automatically learns and adapts to individual occupant preferences over time, eliminating the need for manual policy adjustments or reactive responses to complaints, and autonomously optimizes the balance between comfort and energy use
3Object-affected harmful factors
If facility managers over-react to occupant complaints by setting cooling set points too low or raising heating set points too high, then occupant comfort is temporarily improved, but energy waste increases and new complaints are generated
Solution Approach 1:
The system proactively adjusts temperature setpoints based on predicted occupancy patterns and learned preferences before comfort issues arise, rather than reacting to complaints after they occur, preventing the cycle of over-correction
Solution Approach 2:
The system makes subtle, incremental adjustments to temperature setpoints based on quantitative optimization criteria rather than large reactive changes, maintaining comfort within acceptable ranges while minimizing energy waste from excessive setpoint deviations
4Loss of information
If ad hoc communications are used to gather occupant preferences, then individual comfort needs can be addressed, but the process becomes ineffective and time-consuming for large numbers of occupants
Solution Approach 1:
The system introduces an automated digital intermediary (mobile app interface) that mediates between occupants and facility managers, allowing occupants to input preferences asynchronously without requiring manager time, while the system automatically processes and acts on this information
Solution Approach 2:
The system creates digital copies of occupant profiles and preferences that can be stored, retrieved, and processed automatically, eliminating the need for repeated manual communication and enabling the system to serve hundreds or thousands of occupants simultaneously
Data Source
AI summary
A building has climate control equipment which controls a temperature at different locations. Different locations may be in different control zones controlled by different control devices. An occupant of a location submits a desired location temperature through a user interface on a computing device to a networked server. Setting of a desired temperature is constrained by energy saving policies and by conditions of surrounding locations. An arbitrator device determines based on constraints a new temperature setting. The new temperature setting is accompanied by an energy saving feedback. The occupants confirms the new setting. A climate control device is instructed to apply a device setting to achieve the new temperature. A climate profile of the occupant is learned from previous temperature settings by the occupant.