Electrochromic Device Cloud Forecasting for Faster Tint Response
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Solution Overview
Problem
Conventional smart window systems face inefficiencies due to lag-time in tint changes, leading to increased energy consumption, user frustration, and wear and tear, as they struggle to respond quickly to dynamically changing sky conditions.
Innovation Solution
A system that processes images of sky conditions to isolate cloud pixels, predicts cloud movement relative to sun position, and automatically controls electrochromic devices based on this prediction, reducing the need for manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional smart window systems use manual control or simple automated control, then user comfort can be maintained, but energy consumption increases and response to dynamic sky conditions is slow
Solution Approach 1:
The system performs preliminary actions by predicting future cloud movements and sun positions before they actually occur. The machine learning model forecasts sky conditions ahead of time, allowing the electrochromic device to be pre-adjusted to the appropriate tint level, eliminating lag-time and enabling proactive energy management.
Solution Approach 2:
The system implements continuous feedback loops where sky sensors constantly monitor current sky conditions, the machine learning model processes this data along with historical patterns to predict future conditions, and the electrochromic device adjusts accordingly. This closed-loop feedback system ensures optimal energy efficiency while maintaining rapid response to dynamic changes.
2Loss of energy
If the system responds quickly to changing sky conditions, then energy consumption is reduced, but device complexity and processing requirements increase
Solution Approach 1:
The system performs self-service by using the machine learning model to autonomously predict sky conditions and automatically control the electrochromic device without requiring manual user input. The system serves itself by integrating sky sensing, predictive analytics, and automated control into a unified self-managing system that optimizes energy efficiency independently.
Solution Approach 2:
The patent replaces simple mechanical or rule-based control systems with an intelligent machine learning-based predictive system. This substitution enables more sophisticated energy optimization by using algorithms that learn from historical data and predict future conditions, achieving better energy efficiency without proportionally increasing physical device complexity.
3Duration of action of stationary object
If manual control is used, then device wear and tear is reduced, but user frustration increases due to lag-time
Solution Approach 1:
The system performs preliminary actions by predicting future sky conditions and pre-adjusting the electrochromic device before the actual condition changes occur. This eliminates the frustrating lag-time experience where users manually adjust windows after conditions have already changed, while the automated predictive control actually reduces wear by making smoother, more timely adjustments.
4Ease of operation
If automated control is implemented, then user convenience is improved, but bandwidth and processor overhead increase
Solution Approach 1:
The system applies partial action by using the machine learning model to predict only the critical sky conditions that will significantly impact energy consumption, rather than continuously adjusting for every minor change. This selective approach maintains high user convenience while optimizing bandwidth and processor resource usage by focusing computational effort on meaningful predictions.
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
Significantly reduces energy consumption by allowing more daylighting and avoiding manual control, thus minimizing energy usage, bandwidth, and processor overhead.
Implementation Method 1
An electrochromic glass unit uses electrochromic glass that can change transmissivity with the application of electric current and voltage. The change of transmissivity typically relies on a reversible oxidation of a material.
Data Source
AI summary
A method includes identifying images corresponding to sky conditions. The method further includes isolating cloud pixels from sky pixels in each of the images. Responsive to determining percentage of cloud pixels in one or more of the images meets a threshold value, the method further includes determining predicted cloud movement relative to sun position by tracking similar feature points between two or more images of the images. The method further includes causing a tint level of an electrochromic device to be controlled based on the predicted cloud movement relative to the sun position.