Thermostat Set Point Comparison Using Peer Energy Feedback
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Solution Overview
Problem
Individual thermostat users lack a basis for comparing their energy consumption practices to others, leading to stagnant energy conservation efforts, as they rely on personal experience and intuition to set optimal temperatures without normative guidance or data-driven insights.
Innovation Solution
Network-connected thermostats provide data for peer comparisons, calculating and displaying the relative efficiency of individual thermostat set points against community norms, using historical and real-time data to educate users and encourage energy conservation by visualizing their position on an energy efficiency continuum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If individual thermostat users rely on personal experience and intuition to set temperatures, then they can operate thermostats independently, but they lack a basis for comparing their energy consumption practices to others, leading to stagnant energy conservation efforts
Solution Approach 1:
The system collects thermostat set point data from multiple users and provides comparative feedback to each user about their energy consumption practices relative to peers. This feedback loop enables users to see how their settings compare to others and motivates them to adjust toward more energy-efficient practices, directly addressing the lack of comparative information and improving conservation effectiveness
Solution Approach 2:
The system segments users into comparable groups based on similar characteristics (e.g., geography, building type, occupancy patterns) and provides comparisons within these segments. This segmentation allows for meaningful peer-to-peer comparison by ensuring users are compared with others in similar situations, making the feedback relevant and actionable
2Productivity
If network-connected thermostats collect and process data from multiple users for peer comparisons, then energy conservation effectiveness improves through feedback, but system complexity increases
Solution Approach 1:
The patent introduces a central server or cloud-based platform as an intermediary that collects, processes, and analyzes thermostat data from multiple users. This intermediary handles the complex data aggregation, comparison logic, and feedback generation, while individual thermostats remain relatively simple devices that primarily communicate data and display feedback. This distributes complexity to a dedicated component rather than requiring each thermostat to be highly complex
Solution Approach 2:
The system creates a multi-functional platform that performs multiple tasks: collecting data from various users, storing historical data, analyzing energy consumption patterns, generating comparisons, and providing feedback through different interfaces. This universal platform serves all users simultaneously, amortizing the complexity across a broad user base and making the system scalable
Data Source
AI summary
A thermostat set point insight providing method and system that receives thermostat set point information for a reference population for a user, receives at least one thermostat setting for the user, identifies the set point insight for the user based on the thermostat set point information for the reference population and the at least one thermostat setting for the user, and provides the set point insight to the user.