Thermostat Time Auto-Configuration Using Natural Light Sensing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users often struggle to set the time-of-day on thermostats for HVAC systems due to complexity, especially with different time zones and daylight savings, which hampers elegant control of heating and cooling equipment and leads to reduced energy savings and user satisfaction.
Innovation Solution
A thermostat that can discern and estimate the time-of-day using sensors, such as light sensors to monitor natural light patterns, allowing it to automatically set the time without user input, while also allowing manual override and filtering out man-made lighting confusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually set the time-of-day on thermostats, then the time can be accurately configured, but the operation becomes complex and intimidating due to time zones and daylight savings
Solution Approach 1:
The thermostat automatically determines time-of-day using environmental sensors (light sensors detecting natural light patterns) without requiring user configuration. The system self-configures by monitoring light cycles to estimate time-of-day, eliminating the need for users to manually set time while maintaining accuracy
Solution Approach 2:
The patent replaces manual mechanical time-setting mechanisms with an automated sensing system. Light sensors detect natural light patterns and the processor analyzes these patterns to automatically determine time-of-day, substituting user interaction with sensor-based automation
2Ease of operation
If the thermostat uses sensors to automatically determine time-of-day, then user interaction is simplified, but the device complexity increases
Solution Approach 1:
The light sensor serves multiple functions: it detects natural light patterns for time-of-day determination, and can potentially be used for other environmental monitoring tasks. This multi-functionality justifies the added complexity by providing versatile capabilities within the thermostat system
Solution Approach 2:
The automated time-determination system uses the existing sensor infrastructure of the thermostat to self-configure, eliminating the need for separate manual configuration mechanisms and reducing overall system complexity despite adding sensing capabilities
3Measurement precision
If the thermostat filters sensor input to avoid man-made lighting confusion, then time estimation accuracy improves, but the processing complexity increases
Solution Approach 1:
The processor continuously monitors light sensor patterns and uses feedback mechanisms to distinguish between natural light cycles (sunrise/sunset) and artificial lighting. By analyzing the temporal patterns and characteristics of light input over time, the system learns to filter out artificial lighting interference and accurately estimate time-of-day
Solution Approach 2:
The system processes more light sensor data than strictly necessary by analyzing extended time periods and multiple light pattern characteristics. This excessive processing ensures accurate differentiation between natural and artificial light, improving time estimation reliability
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient energy management by automatically adjusting HVAC systems based on estimated time-of-day, optimizing energy usage while maintaining comfort, and simplifying user interaction with a visually appealing and intuitive interface.
Implementation Method 1
a light sensor can monitor natural light to understand the cycle of sun with respect to the installation location
Data Source
AI summary
Provided according to some embodiments is a thermostat is capable of discerning the time-of-day without external input. Should the user fail to set the time, the thermostat uses one or more sensors to determine the time-of-day through a variety of techniques. In one example, a light sensor can monitor natural light to understand the cycle of sun with respect to the installation location. From the cycle of natural light a latitude, time-of-year, time-of-day, etc. can be estimated through processing sensor information over time. Should the thermostat have its time manually set or gathered from the network, it would override the estimated time-of-day. Techniques can be used to filter input from the one or more sensors to avoid confusion from other inputs, for example, man-made lighting.