Tintable Window Forecasting for Site-Specific Energy Control
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Solution Overview
Problem
Electrochromic windows have not realized their full commercial potential due to limited advancements in controlling tint based on environmental conditions, leading to inefficiencies in energy savings and user comfort.
Innovation Solution
A control system incorporating neural networks, such as LSTM and DNN, processes sensor data from photosensors and infrared sensors to forecast environmental conditions and adjust tintable window states accordingly, using site-specific and seasonally differentiated weather data to optimize tint levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional reactive control methods are used for electrochromic windows, then the system is simple to implement, but energy savings and user comfort are insufficient
Solution Approach 1:
The forecasting module predicts future environmental conditions (temperature, humidity, light levels) before they occur, allowing the window control system to proactively adjust tint levels in advance. This preliminary action enables the system to prepare for upcoming conditions rather than merely reacting to current states, thereby improving energy efficiency while maintaining manageable complexity through automated prediction algorithms.
Solution Approach 2:
The system incorporates sensor feedback from environmental sensors that continuously monitor temperature, humidity, and light conditions. This feedback loop provides real-time data to the forecasting module, which uses machine learning algorithms to predict future conditions and adjust window tint accordingly. The feedback mechanism enables adaptive control that optimizes energy savings while managing system complexity through data-driven decision-making.
2Adaptability or versatility
If real-time sensor data alone is used for control, then the response is immediate, but the system cannot anticipate future environmental changes
Solution Approach 1:
The forecasting module performs preliminary predictions of future environmental conditions using sensor data and machine learning models. By anticipating temperature changes, light levels, and humidity variations before they occur, the system can pre-adjust window tint levels, thereby improving adaptability without sacrificing response time. The prediction capability allows the system to act in advance rather than waiting for conditions to change.
Solution Approach 2:
The control system dynamically adjusts its behavior based on both current sensor readings and forecasted future conditions. The forecasting module continuously updates predictions as new sensor data arrives, allowing the system to adapt to changing environmental patterns while maintaining real-time responsiveness. This dynamic approach enables the system to balance predictive capability with immediate response requirements.
3Productivity
If generic control algorithms are used, then the system is easier to implement, but it cannot optimize for site-specific weather patterns
Solution Approach 1:
The forecasting module is configured to learn and adapt to local weather patterns, seasonal variations, and site-specific environmental conditions. By tailoring the prediction algorithms to local characteristics rather than using generic controls, the system optimizes energy efficiency for each specific location. The machine learning models are trained on local historical data, enabling them to capture regional weather patterns and provide location-optimized control recommendations.
Solution Approach 2:
The system dynamically adjusts control parameters based on forecasted environmental conditions, including temperature thresholds, humidity levels, and light intensity ranges. By changing these parameters in response to predicted future conditions rather than maintaining fixed settings, the system achieves superior energy optimization. The ability to modify control parameters based on forecasts allows the system to adapt to varying weather patterns while maintaining implementation feasibility through automated parameter adjustment.
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
Enhances the ability of electrochromic windows to dynamically adjust to environmental conditions, improving energy efficiency and user comfort by predicting and proactively controlling tint based on future weather patterns.
Implementation Method 1
one or more neural network coupled to the one or more window controller, wherein neural network comprises control logic configured to process the first output and to provide a second output representative of a forecast of a future environmental condition
Implementation Method 2
Electrochromism is a phenomenon in which a material exhibits a (e.g., reversible) electrochemically-mediated change in an optical property when placed in a different electronic state. One electrochromic material is tungsten oxide (WO3). Tungsten oxide is a cathodic electrochromic material in which a coloration transition (e.g., transparent to blue) occurs by electrochemical reduction.
Data Source
AI summary
Disclosed herein are systems, apparatuses, methods, and non-transitory computer readable media related to controlling tint of tintable window(s) that include various predictive modules, and quality assurance related modules.