Predictive Electrochromic Window Control Using Neural Forecasts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing electrochromic windows suffer from various issues, including inefficient control methods and limited integration with advanced technologies, which hinder their commercial potential.
Innovation Solution
A control system for tintable windows that incorporates a neural network, specifically an LSTM or DNN, to process signals from sensors and forecast environmental conditions, enabling predictive control of window tint levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional control methods are used for electrochromic windows, then the system is simple to implement, but energy efficiency and user comfort are insufficient
Solution Approach 1:
The system performs preliminary actions by forecasting environmental conditions (sun position, cloud cover, temperature) before they actually occur. The neural network analyzes historical and current sensor data to predict future conditions, allowing the window control system to proactively adjust tint levels in anticipation of changing environmental factors, thereby improving energy efficiency before problems arise.
Solution Approach 2:
The patent introduces external 3D modeling and neural networks as intermediary components between sensors and window control. These intermediaries process sensor data, integrate it with environmental forecasts, and generate optimized control commands, bridging the gap between raw environmental data and effective window control decisions.
2Measurement precision
If advanced neural network forecasting is implemented, then predictive control accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the forecasting task into separate neural network models for different environmental parameters (sun position, cloud cover, temperature). Each model specializes in predicting specific conditions, improving overall accuracy while allowing modular development and deployment. This segmentation also enables parallel processing and reduces the computational burden on any single model.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based control systems with neural network-based predictive control. The neural networks substitute for complex if-then logic and manual control mechanisms, providing more accurate and adaptive forecasting through learned patterns from historical data, thereby improving measurement precision through intelligent algorithms.
3Productivity
If real-time sensor processing with neural networks is used, then control responsiveness improves, but computational load and processing time increase
Solution Approach 1:
The system implements periodic action by updating neural network predictions at optimized intervals rather than continuously. The control system processes sensor data and generates forecast updates at strategic time points, balancing responsiveness with computational efficiency. This periodic processing reduces power consumption while maintaining adequate control responsiveness for energy management purposes.
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
The system effectively controls tintable windows based on predicted environmental conditions, enhancing energy efficiency and user comfort while realizing the full potential of electrochromic technology.
Implementation Method 1
A control system for tintable windows that incorporates a neural network, specifically an LSTM or DNN, to process signals from sensors and forecast environmental conditions
Implementation Method 2
Electrochromism is a phenomenon in which a material exhibits a reversible electrochemically-mediated change in an optical property when placed in a different electronic state, typically by being subjected to a voltage change
Data Source
Figure 1A~1B
Figure 1C
Figure 2A~2B
AI summary
A system for controlling tinting of one or more zones of windows in a building based on predictions of future environmental conditions.